Added alternative version of scikits statsmodels
Per.Andreas.Brodtkorb
13 years ago
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5a2c8d79b3
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df9e95f0b5
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*.py[oc]
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# setup.py working directory
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build
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# setup.py dist directory
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./dist
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# Editor temporary/working/backup files
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*$
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.*.sw[nop]
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.sw[nop]
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*~
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[#]*#
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.#*
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*.bak
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*.tmp
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*.tgz
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*.rej
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*.org
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.project
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*.diff
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.settings/
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*.svn/
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*.log.py
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# Egg metadata
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./*.egg-info
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# The shelf plugin uses this dir
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./.shelf
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# Mac droppings
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.DS_Store
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help
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# .coveragerc to control coverage.py
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[run]
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branch = False
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[report]
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# Regexes for lines to exclude from consideration
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exclude_lines =
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# Have to re-enable the standard pragma
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pragma: no cover
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# Don't complain about missing debug-only code:
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def __repr__
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if self\.debug
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# Don't complain if tests don't hit defensive assertion code:
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raise AssertionError
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raise NotImplementedError
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# Don't complain if non-runnable code isn't run:
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if 0:
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if __name__ == .__main__.:
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ignore_errors = False
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[html]
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directory = coverage_html_report
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* text=auto
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*.py[oc]
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# setup.py working directory
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build
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# setup.py dist directory
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./dist
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# repository directories for bzr-git
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.bzr
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.git
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marks.git
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marks.bzr
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# Editor temporary/working/backup files
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*$
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.*.sw[nop]
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.sw[nop]
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*~
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[#]*#
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.#*
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*.bak
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*.tmp
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*.tgz
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*.rej
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*.org
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.project
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*.diff
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.settings/
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*.svn/
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*.log.py
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# Egg metadata
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./*.egg-info
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# The shelf plugin uses this dir
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./.shelf
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# Mac droppings
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.DS_Store
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help
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# Project specific
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scikits/statsmodels/version.py
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Release History
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===============
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trunk for 0.4.0
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---------------
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* tools.tools.ECDF -> distributions.ECDF
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* tools.tools.monotone_fn_inverter -> distributions.monotone_fn_inverter
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* tools.tools.StepFunction -> distributions.StepFunction
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0.3.1
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-----
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* Removed academic-only WFS dataset.
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* Fix easy_install issue on Windows.
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0.3.0
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-----
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*Changes that break backwards compatibility*
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Added api.py for importing. So the new convention for importing is::
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import scikits.statsmodels.api as sm
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Importing from modules directly now avoids unnecessary imports and increases
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the import speed if a library or user only needs specific functions.
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* sandbox/output.py -> iolib/table.py
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* lib/io.py -> iolib/foreign.py (Now contains Stata .dta format reader)
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* family -> families
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* families.links.inverse -> families.links.inverse_power
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* Datasets' Load class is now load function.
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* regression.py -> regression/linear_model.py
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* discretemod.py -> discrete/discrete_model.py
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* rlm.py -> robust/robust_linear_model.py
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* glm.py -> genmod/generalized_linear_model.py
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* model.py -> base/model.py
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* t() method -> tvalues attribute (t() still exists but raises a warning)
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*Main changes and additions*
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* Numerous bugfixes.
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* Time Series Analysis model (tsa)
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- Vector Autoregression Models VAR (tsa.VAR)
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- Autogressive Models AR (tsa.AR)
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- Autoregressive Moving Average Models ARMA (tsa.ARMA)
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optionally uses Cython for Kalman Filtering
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use setup.py install with option --with-cython
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- Baxter-King band-pass filter (tsa.filters.bkfilter)
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- Hodrick-Prescott filter (tsa.filters.hpfilter)
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- Christiano-Fitzgerald filter (tsa.filters.cffilter)
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* Improved maximum likelihood framework uses all available scipy.optimize solvers
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* Refactor of the datasets sub-package.
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* Added more datasets for examples.
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* Removed RPy dependency for running the test suite.
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* Refactored the test suite.
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* Refactored codebase/directory structure.
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* Support for offset and exposure in GLM.
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* Removed data_weights argument to GLM.fit for Binomial models.
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* New statistical tests, especially diagnostic and specification tests
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* Multiple test correction
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* General Method of Moment framework in sandbox
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* Improved documentation
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* and other additions
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0.2.0
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-----
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*Main changes*
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* renames for more consistency
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RLM.fitted_values -> RLM.fittedvalues
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GLMResults.resid_dev -> GLMResults.resid_deviance
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* GLMResults, RegressionResults:
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lazy calculations, convert attributes to properties with _cache
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* fix tests to run without rpy
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* expanded examples in examples directory
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* add PyDTA to lib.io -- functions for reading Stata .dta binary files
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and converting
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them to numpy arrays
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* made tools.categorical much more robust
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* add_constant now takes a prepend argument
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* fix GLS to work with only a one column design
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*New*
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* add four new datasets
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- A dataset from the American National Election Studies (1996)
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- Grunfeld (1950) investment data
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- Spector and Mazzeo (1980) program effectiveness data
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- A US macroeconomic dataset
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* add four new Maximum Likelihood Estimators for models with a discrete
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dependent variables with examples
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- Logit
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- Probit
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- MNLogit (multinomial logit)
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- Poisson
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*Sandbox*
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* add qqplot in sandbox.graphics
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* add sandbox.tsa (time series analysis) and sandbox.regression (anova)
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* add principal component analysis in sandbox.tools
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* add Seemingly Unrelated Regression (SUR) and Two-Stage Least Squares
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for systems of equations in sandbox.sysreg.Sem2SLS
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* add restricted least squares (RLS)
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0.1.0b1
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-------
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* initial release
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@ -0,0 +1,216 @@
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The license of scikits.statsmodels can be found in LICENSE.txt
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scikits.statsmodels contains code or derivative code from several other
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packages. Some modules also note the author of individual contributions, or
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author of code that formed the basis for the derived or translated code.
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The copyright statements for the datasets are attached to the individual
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datasets, most datasets are in public domain, and we don't claim any copyright
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on any of them.
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In the following, we collect copyright statements of code from other packages,
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all of which are either a version of BSD or MIT licensed:
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numpy
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scipy
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pandas
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matplotlib
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scikits.learn
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numpy (scikits.statsmodels.compatnp contains copy of entire model)
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------------------------------------------------------------------
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Copyright (c) 2005-2009, NumPy Developers.
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All rights reserved.
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||||||
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Redistribution and use in source and binary forms, with or without
|
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|
modification, are permitted provided that the following conditions are
|
||||||
|
met:
|
||||||
|
|
||||||
|
* Redistributions of source code must retain the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer.
|
||||||
|
|
||||||
|
* Redistributions in binary form must reproduce the above
|
||||||
|
copyright notice, this list of conditions and the following
|
||||||
|
disclaimer in the documentation and/or other materials provided
|
||||||
|
with the distribution.
|
||||||
|
|
||||||
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* Neither the name of the NumPy Developers nor the names of any
|
||||||
|
contributors may be used to endorse or promote products derived
|
||||||
|
from this software without specific prior written permission.
|
||||||
|
|
||||||
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||||
|
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||||
|
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||||
|
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
|
||||||
|
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
|
||||||
|
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
|
||||||
|
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
|
||||||
|
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
|
||||||
|
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
||||||
|
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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||||||
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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---------------------------------------------------------------------
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scipy
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-----
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Copyright (c) 2001, 2002 Enthought, Inc.
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All rights reserved.
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Copyright (c) 2003-2009 SciPy Developers.
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
|
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modification, are permitted provided that the following conditions are met:
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|
|
||||||
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a. Redistributions of source code must retain the above copyright notice,
|
||||||
|
this list of conditions and the following disclaimer.
|
||||||
|
b. Redistributions in binary form must reproduce the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer in the
|
||||||
|
documentation and/or other materials provided with the distribution.
|
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|
c. Neither the name of the Enthought nor the names of its contributors
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|
may be used to endorse or promote products derived from this software
|
||||||
|
without specific prior written permission.
|
||||||
|
|
||||||
|
|
||||||
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||||
|
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
|
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ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||||
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||||
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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||||||
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LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
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OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
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DAMAGE.
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---------------------------------------------------------------------------
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pandas
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------
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Copyright (c) 2008-2009 AQR Capital Management, LLC
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
|
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|
modification, are permitted provided that the following conditions are
|
||||||
|
met:
|
||||||
|
|
||||||
|
* Redistributions of source code must retain the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer.
|
||||||
|
|
||||||
|
* Redistributions in binary form must reproduce the above
|
||||||
|
copyright notice, this list of conditions and the following
|
||||||
|
disclaimer in the documentation and/or other materials provided
|
||||||
|
with the distribution.
|
||||||
|
|
||||||
|
* Neither the name of the copyright holder nor the names of any
|
||||||
|
contributors may be used to endorse or promote products derived
|
||||||
|
from this software without specific prior written permission.
|
||||||
|
|
||||||
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS
|
||||||
|
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||||
|
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||||
|
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
|
||||||
|
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
|
||||||
|
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
|
||||||
|
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
|
||||||
|
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
|
||||||
|
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
||||||
|
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||||
|
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
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|
----------------------------------------------------------------------
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matplotlib (copied from license.py)
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LICENSE AGREEMENT FOR MATPLOTLIB %(version)s
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||||||
|
--------------------------------------
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|
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||||||
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1. This LICENSE AGREEMENT is between John D. Hunter ("JDH"), and the
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||||||
|
Individual or Organization ("Licensee") accessing and otherwise using
|
||||||
|
matplotlib software in source or binary form and its associated
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||||||
|
documentation.
|
||||||
|
|
||||||
|
2. Subject to the terms and conditions of this License Agreement, JDH
|
||||||
|
hereby grants Licensee a nonexclusive, royalty-free, world-wide license
|
||||||
|
to reproduce, analyze, test, perform and/or display publicly, prepare
|
||||||
|
derivative works, distribute, and otherwise use matplotlib %(version)s
|
||||||
|
alone or in any derivative version, provided, however, that JDH's
|
||||||
|
License Agreement and JDH's notice of copyright, i.e., "Copyright (c)
|
||||||
|
2002-%(year)d John D. Hunter; All Rights Reserved" are retained in
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|
matplotlib %(version)s alone or in any derivative version prepared by
|
||||||
|
Licensee.
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||||||
|
|
||||||
|
3. In the event Licensee prepares a derivative work that is based on or
|
||||||
|
incorporates matplotlib %(version)s or any part thereof, and wants to
|
||||||
|
make the derivative work available to others as provided herein, then
|
||||||
|
Licensee hereby agrees to include in any such work a brief summary of
|
||||||
|
the changes made to matplotlib %(version)s.
|
||||||
|
|
||||||
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4. JDH is making matplotlib %(version)s available to Licensee on an "AS
|
||||||
|
IS" basis. JDH MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
|
||||||
|
IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, JDH MAKES NO AND
|
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DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
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||||||
|
FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF MATPLOTLIB %(version)s
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WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
|
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||||||
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5. JDH SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB
|
||||||
|
%(version)s FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR
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||||||
|
LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING
|
||||||
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MATPLOTLIB %(version)s, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF
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|
THE POSSIBILITY THEREOF.
|
||||||
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||||||
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6. This License Agreement will automatically terminate upon a material
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||||||
|
breach of its terms and conditions.
|
||||||
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||||||
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7. Nothing in this License Agreement shall be deemed to create any
|
||||||
|
relationship of agency, partnership, or joint venture between JDH and
|
||||||
|
Licensee. This License Agreement does not grant permission to use JDH
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||||||
|
trademarks or trade name in a trademark sense to endorse or promote
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||||||
|
products or services of Licensee, or any third party.
|
||||||
|
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||||||
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8. By copying, installing or otherwise using matplotlib %(version)s,
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||||||
|
Licensee agrees to be bound by the terms and conditions of this License
|
||||||
|
Agreement.
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|
--------------------------------------------------------------------------
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|
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scikits.learn
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|
-------------
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New BSD License
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||||||
|
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Copyright (c) 2007 - 2010 Scikit-Learn Developers.
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All rights reserved.
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|
||||||
|
Redistribution and use in source and binary forms, with or without
|
||||||
|
modification, are permitted provided that the following conditions are met:
|
||||||
|
|
||||||
|
a. Redistributions of source code must retain the above copyright notice,
|
||||||
|
this list of conditions and the following disclaimer.
|
||||||
|
b. Redistributions in binary form must reproduce the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer in the
|
||||||
|
documentation and/or other materials provided with the distribution.
|
||||||
|
c. Neither the name of the Scikit-learn Developers nor the names of
|
||||||
|
its contributors may be used to endorse or promote products
|
||||||
|
derived from this software without specific prior written
|
||||||
|
permission.
|
||||||
|
|
||||||
|
|
||||||
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||||
|
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||||
|
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||||
|
ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
|
||||||
|
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||||
|
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||||
|
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||||
|
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||||
|
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
|
||||||
|
OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
|
||||||
|
DAMAGE.
|
||||||
|
---------------------------------------------------------------------------
|
||||||
|
|
||||||
@ -0,0 +1,75 @@
|
|||||||
|
Dependencies
|
||||||
|
------------
|
||||||
|
|
||||||
|
Python >= 2.5
|
||||||
|
NumPy >= 1.4.0
|
||||||
|
SciPy >= 0.7
|
||||||
|
|
||||||
|
Optional Dependencies
|
||||||
|
---------------------
|
||||||
|
|
||||||
|
Matplotlib is needed for plotting functionality and running many of the examples
|
||||||
|
|
||||||
|
http://matplotlib.sourceforge.net/
|
||||||
|
|
||||||
|
To build the documentation you will need Sphinx
|
||||||
|
|
||||||
|
http://sphinx.pocoo.org/
|
||||||
|
|
||||||
|
The documentation is available online as mentioned below.
|
||||||
|
|
||||||
|
To run the test suite you will need nose
|
||||||
|
|
||||||
|
http://somethingaboutorange.com/mrl/projects/nose/
|
||||||
|
|
||||||
|
|
||||||
|
Easy Install
|
||||||
|
------------
|
||||||
|
|
||||||
|
To get the latest release using easy_install you need setuptools (easy_install)
|
||||||
|
|
||||||
|
http://peak.telecommunity.com/DevCenter/EasyInstall
|
||||||
|
|
||||||
|
Then you can do (with proper permissions)
|
||||||
|
|
||||||
|
easy_install -U scikits.statsmodels
|
||||||
|
|
||||||
|
Ubuntu/Debian
|
||||||
|
-------------
|
||||||
|
|
||||||
|
On (X)ubuntu you can get dependencies through
|
||||||
|
|
||||||
|
sudo apt-get install python python-setuptools python-numpy python-scipy
|
||||||
|
|
||||||
|
You may install with easy_install, from source as mentioned below, or
|
||||||
|
from the NeuroDebian repository: http://neuro.debian.net
|
||||||
|
|
||||||
|
Installing from Source
|
||||||
|
----------------------
|
||||||
|
|
||||||
|
Download and extract the source distribution from PyPI or github
|
||||||
|
|
||||||
|
PyPI: http://pypi.python.org/pypi/scikits.statsmodels
|
||||||
|
Github: https://github.com/statsmodels/statsmodels/archives/master
|
||||||
|
|
||||||
|
Or clone the bleeding edge code from our repository on github at
|
||||||
|
|
||||||
|
https://github.com/statsmodels/statsmodels
|
||||||
|
|
||||||
|
In the statsmodels directory do (with proper permissions)
|
||||||
|
|
||||||
|
python setup.py install
|
||||||
|
|
||||||
|
For the 0.3.0 release, there is some code written using Cython. If you have
|
||||||
|
a C compiler, you can do
|
||||||
|
|
||||||
|
python setup.py --with-cython
|
||||||
|
python setup.py install
|
||||||
|
|
||||||
|
Documentation
|
||||||
|
-------------
|
||||||
|
|
||||||
|
You may find more information about the project and installation in our
|
||||||
|
documentation
|
||||||
|
|
||||||
|
http://statsmodels.sourceforge.net/
|
||||||
@ -0,0 +1,35 @@
|
|||||||
|
Copyright (C) 2006, Jonathan E. Taylor
|
||||||
|
All rights reserved.
|
||||||
|
|
||||||
|
Copyright (c) 2006-2008 Scipy Developers.
|
||||||
|
All rights reserved.
|
||||||
|
|
||||||
|
Copyright (c) 2009 Statsmodels Developers.
|
||||||
|
All rights reserved.
|
||||||
|
|
||||||
|
|
||||||
|
Redistribution and use in source and binary forms, with or without
|
||||||
|
modification, are permitted provided that the following conditions are met:
|
||||||
|
|
||||||
|
a. Redistributions of source code must retain the above copyright notice,
|
||||||
|
this list of conditions and the following disclaimer.
|
||||||
|
b. Redistributions in binary form must reproduce the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer in the
|
||||||
|
documentation and/or other materials provided with the distribution.
|
||||||
|
c. Neither the name of Statsmodels nor the names of its contributors
|
||||||
|
may be used to endorse or promote products derived from this software
|
||||||
|
without specific prior written permission.
|
||||||
|
|
||||||
|
|
||||||
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||||
|
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||||
|
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||||
|
ARE DISCLAIMED. IN NO EVENT SHALL STATSMODELS OR CONTRIBUTORS BE LIABLE FOR
|
||||||
|
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||||
|
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||||
|
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||||
|
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||||
|
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
|
||||||
|
OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
|
||||||
|
DAMAGE.
|
||||||
|
|
||||||
@ -0,0 +1,31 @@
|
|||||||
|
global-include *.csv *.py *.txt *.pyx *.c
|
||||||
|
#scikits*.*
|
||||||
|
include MANIFEST.in
|
||||||
|
|
||||||
|
#exclude scikits/statsmodels/docs/build/htmlhelp*
|
||||||
|
recursive-exclude build *
|
||||||
|
recursive-exclude dist *
|
||||||
|
recursive-exclude tools *
|
||||||
|
|
||||||
|
graft scikits/statsmodels/datasets
|
||||||
|
graft scikits/statsmodels/tests
|
||||||
|
graft scikits/statsmodels/sandbox/regression/data
|
||||||
|
graft scikits/statsmodels/sandbox/tests
|
||||||
|
graft scikits/statsmodels/sandbox/tsa/examples
|
||||||
|
graft scikits/statsmodels/tsa/vector_ar/data
|
||||||
|
recursive-include scikits/statsmodels/docs/source *
|
||||||
|
exclude scikits/statsmodels/docs/source/generated/*
|
||||||
|
recursive-include scikits/statsmodels/docs/sphinxext *
|
||||||
|
recursive-exclude scikits/statsmodels/docs/build *
|
||||||
|
#recursive-include scikits/statsmodels/docs/build/html *
|
||||||
|
recursive-exclude scikits/statsmodels/docs/build/htmlhelp *
|
||||||
|
include scikits/statsmodels/docs/build/htmlhelp/statsmodelsdoc.chm
|
||||||
|
include scikits/statsmodels/docs/make.bat
|
||||||
|
include scikits/statsmodels/docs/Makefile
|
||||||
|
#include scikits/statsmodels/docs mak*
|
||||||
|
#include scikits/statsmodels/docs GLM*
|
||||||
|
|
||||||
|
#missed files: .npz, .npy
|
||||||
|
include scikits/statsmodels/tsa/vector_ar/tests/results/vars_results.npz
|
||||||
|
include scikits/statsmodels/iolib/tests/results/*
|
||||||
|
global-exclude *~ *.swp *.pyc *.bak
|
||||||
@ -0,0 +1,121 @@
|
|||||||
|
What it is
|
||||||
|
==========
|
||||||
|
|
||||||
|
Statsmodels is a Python package that provides a complement to scipy for
|
||||||
|
statistical computations including descriptive statistics and
|
||||||
|
estimation of statistical models.
|
||||||
|
|
||||||
|
Main Features
|
||||||
|
=============
|
||||||
|
|
||||||
|
* regression: Generalized least squares (including weighted least squares and
|
||||||
|
least squares with autoregressive errors), ordinary least squares.
|
||||||
|
* glm: Generalized linear models with support for all of the one-parameter
|
||||||
|
exponential family distributions.
|
||||||
|
* discrete choice models: Poisson, probit, logit, multinomial logit
|
||||||
|
* rlm: Robust linear models with support for several M-estimators.
|
||||||
|
* tsa: Time series analysis models, including ARMA, AR, VAR
|
||||||
|
* nonparametric : (Univariate) kernel density estimators
|
||||||
|
* datasets: Datasets to be distributed and used for examples and in testing.
|
||||||
|
* PyDTA: Tools for reading Stata .dta files into numpy arrays.
|
||||||
|
* stats: a wide range of statistical tests
|
||||||
|
* sandbox: There is also a sandbox which contains code for generalized additive
|
||||||
|
models (untested), mixed effects models, cox proportional hazards model (both
|
||||||
|
are untested and still dependent on the nipy formula framework), generating
|
||||||
|
descriptive statistics, and printing table output to ascii, latex, and html.
|
||||||
|
There is also experimental code for systems of equations regression,
|
||||||
|
time series models, panel data estimators and information theoretic measures.
|
||||||
|
None of this code is considered "production ready".
|
||||||
|
|
||||||
|
|
||||||
|
Where to get it
|
||||||
|
===============
|
||||||
|
|
||||||
|
Development branches will be on Github. This is where to go to get the most
|
||||||
|
up to date code in the trunk branch. Experimental code is hosted here
|
||||||
|
in branches and in developer forks. This code is merged to master often. We
|
||||||
|
try to make sure that the master branch is always stable.
|
||||||
|
|
||||||
|
https://www.github.com/statsmodels/statsmodels
|
||||||
|
|
||||||
|
Source download of stable tags will be on SourceForge.
|
||||||
|
|
||||||
|
https://sourceforge.net/projects/statsmodels/
|
||||||
|
|
||||||
|
or
|
||||||
|
|
||||||
|
PyPi: http://pypi.python.org/pypi/scikits.statsmodels/
|
||||||
|
|
||||||
|
|
||||||
|
Installation from sources
|
||||||
|
=========================
|
||||||
|
|
||||||
|
In the top directory, just do::
|
||||||
|
|
||||||
|
python setup.py install
|
||||||
|
|
||||||
|
See INSTALL.txt for requirements or
|
||||||
|
|
||||||
|
http://statsmodels.sourceforge.net/
|
||||||
|
|
||||||
|
For more information.
|
||||||
|
|
||||||
|
|
||||||
|
License
|
||||||
|
=======
|
||||||
|
|
||||||
|
Simplified BSD
|
||||||
|
|
||||||
|
|
||||||
|
Documentation
|
||||||
|
=============
|
||||||
|
|
||||||
|
The official documentation is hosted on SourceForge.
|
||||||
|
|
||||||
|
http://statsmodels.sourceforge.net/
|
||||||
|
|
||||||
|
The sphinx docs are currently undergoing a lot of work. They are not yet
|
||||||
|
comprehensive, but should get you started.
|
||||||
|
|
||||||
|
Our blog will continue to be updated as we make progress on the code.
|
||||||
|
|
||||||
|
http://scipystats.blogspot.com
|
||||||
|
|
||||||
|
|
||||||
|
Windows Help
|
||||||
|
============
|
||||||
|
The source distribution for Windows includes a htmlhelp file (statsmodels.chm).
|
||||||
|
This can be opened from the python interpreter ::
|
||||||
|
|
||||||
|
>>> import scikits.statsmodels.api as sm
|
||||||
|
>>> sm.open_help()
|
||||||
|
|
||||||
|
|
||||||
|
Discussion and Development
|
||||||
|
==========================
|
||||||
|
|
||||||
|
All chatter will take place on the or scipy-user mailing list. We are very
|
||||||
|
interested in receiving feedback about usability, suggestions for improvements,
|
||||||
|
and bug reports via the mailing list or the bug tracker at
|
||||||
|
|
||||||
|
https://github.com/statsmodels/statsmodels/issues
|
||||||
|
|
||||||
|
There is also a google group at
|
||||||
|
|
||||||
|
http://groups.google.com/group/pystatsmodels
|
||||||
|
|
||||||
|
to discuss development and design issues that are deemed to be too specialized
|
||||||
|
for the scipy-dev/user list.
|
||||||
|
|
||||||
|
|
||||||
|
Python 3
|
||||||
|
========
|
||||||
|
|
||||||
|
scikits.statsmodels has been ported and tested for Python 3.2. Python 3
|
||||||
|
version of the code can be obtained by running 2to3.py over the entire
|
||||||
|
statsmodels source. The numerical core of statsmodels worked almost without
|
||||||
|
changes, however there can be problems with data input and plotting.
|
||||||
|
The STATA file reader and writer in iolib.foreign has not been ported yet.
|
||||||
|
And there are still some problems with the matplotlib version for Python 3
|
||||||
|
that was used in testing. Running the test suite with Python 3.2 shows some
|
||||||
|
errors related to foreign and matplotlib.
|
||||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
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@ -0,0 +1 @@
|
|||||||
|
__import__('pkg_resources').declare_namespace(__name__)
|
||||||
@ -0,0 +1,36 @@
|
|||||||
|
Copyright (C) 2006, Jonathan E. Taylor
|
||||||
|
All rights reserved.
|
||||||
|
|
||||||
|
Copyright (c) 2006-2008 Scipy Developers.
|
||||||
|
All rights reserved.
|
||||||
|
|
||||||
|
Copyright (c) 2009 Statsmodels Developers.
|
||||||
|
All rights reserved.
|
||||||
|
|
||||||
|
|
||||||
|
Redistribution and use in source and binary forms, with or without
|
||||||
|
modification, are permitted provided that the following conditions are met:
|
||||||
|
|
||||||
|
1. Redistributions of source code must retain the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer.
|
||||||
|
|
||||||
|
2. Redistributions in binary form must reproduce the above
|
||||||
|
copyright notice, this list of conditions and the following
|
||||||
|
disclaimer in the documentation and/or other materials provided
|
||||||
|
with the distribution.
|
||||||
|
|
||||||
|
3. The name of the author may not be used to endorse or promote
|
||||||
|
products derived from this software without specific prior
|
||||||
|
written permission.
|
||||||
|
|
||||||
|
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
|
||||||
|
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
||||||
|
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||||
|
DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
|
||||||
|
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
|
||||||
|
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||||
|
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
|
||||||
|
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
||||||
|
STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
|
||||||
|
IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||||
|
POSSIBILITY OF SUCH DAMAGE.
|
||||||
@ -0,0 +1,19 @@
|
|||||||
|
Tests TODO
|
||||||
|
----------
|
||||||
|
Test I/O of models wrt array types, dimensions
|
||||||
|
- add checks in all top class for data
|
||||||
|
|
||||||
|
Known Issues
|
||||||
|
----------
|
||||||
|
Need to clip mu's in GLM to avoid np.log(0), etc. (done for gamma)
|
||||||
|
Regression will not work with a 1d array for exog (pinv needs two), then
|
||||||
|
other calculations need checking and changing
|
||||||
|
|
||||||
|
TODO
|
||||||
|
-----
|
||||||
|
Make a recarray dataset and masked dataset for testing and development
|
||||||
|
Rename bse
|
||||||
|
Add tvalues attribute to results instead of calling t method?
|
||||||
|
|
||||||
|
|
||||||
|
note the tests requirements somewhere (rpy, R, car library)
|
||||||
@ -0,0 +1,77 @@
|
|||||||
|
#
|
||||||
|
# models - Statistical Models
|
||||||
|
#
|
||||||
|
from __future__ import with_statement
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
#from version import __version__
|
||||||
|
#from info import __doc__
|
||||||
|
|
||||||
|
#from regression import *
|
||||||
|
#from genmod.glm import *
|
||||||
|
#from robust.rlm import *
|
||||||
|
#from discrete.discretemod import *
|
||||||
|
#import tsa
|
||||||
|
#from tools.tools import add_constant, chain_dot
|
||||||
|
#import base.model
|
||||||
|
#import tools.tools
|
||||||
|
#import datasets
|
||||||
|
#import glm.families
|
||||||
|
#import stats.stattools
|
||||||
|
#import iolib
|
||||||
|
|
||||||
|
from numpy import errstate
|
||||||
|
#__all__ = filter(lambda s:not s.startswith('_'),dir())
|
||||||
|
|
||||||
|
from numpy.testing import Tester
|
||||||
|
class NoseWrapper(Tester):
|
||||||
|
'''
|
||||||
|
This is simply a monkey patch for numpy.testing.Tester.
|
||||||
|
|
||||||
|
It allows extra_argv to be changed from its default None to ['--exe'] so
|
||||||
|
that the tests can be run the same across platforms. It also takes kwargs
|
||||||
|
that are passed to numpy.errstate to suppress floating point warnings.
|
||||||
|
'''
|
||||||
|
def test(self, label='fast', verbose=1, extra_argv=['--exe'], doctests=False,
|
||||||
|
coverage=False, **kwargs):
|
||||||
|
''' Run tests for module using nose
|
||||||
|
|
||||||
|
%(test_header)s
|
||||||
|
doctests : boolean
|
||||||
|
If True, run doctests in module, default False
|
||||||
|
coverage : boolean
|
||||||
|
If True, report coverage of NumPy code, default False
|
||||||
|
(Requires the coverage module:
|
||||||
|
http://nedbatchelder.com/code/modules/coverage.html)
|
||||||
|
kwargs
|
||||||
|
Passed to numpy.errstate. See its documentation for details.
|
||||||
|
'''
|
||||||
|
|
||||||
|
# cap verbosity at 3 because nose becomes *very* verbose beyond that
|
||||||
|
verbose = min(verbose, 3)
|
||||||
|
|
||||||
|
from numpy.testing import utils
|
||||||
|
utils.verbose = verbose
|
||||||
|
|
||||||
|
if doctests:
|
||||||
|
print "Running unit tests and doctests for %s" % self.package_name
|
||||||
|
else:
|
||||||
|
print "Running unit tests for %s" % self.package_name
|
||||||
|
|
||||||
|
self._show_system_info()
|
||||||
|
|
||||||
|
# reset doctest state on every run
|
||||||
|
import doctest
|
||||||
|
doctest.master = None
|
||||||
|
|
||||||
|
argv, plugins = self.prepare_test_args(label, verbose, extra_argv,
|
||||||
|
doctests, coverage)
|
||||||
|
from numpy.testing.noseclasses import NumpyTestProgram
|
||||||
|
from warnings import simplefilter #, catch_warnings
|
||||||
|
with errstate(**kwargs):
|
||||||
|
## with catch_warnings():
|
||||||
|
simplefilter('ignore', category=DeprecationWarning)
|
||||||
|
t = NumpyTestProgram(argv=argv, exit=False, plugins=plugins)
|
||||||
|
return t.result
|
||||||
|
test = NoseWrapper().test
|
||||||
@ -0,0 +1,32 @@
|
|||||||
|
import iolib, datasets, tools
|
||||||
|
from tools.tools import add_constant, categorical
|
||||||
|
import regression
|
||||||
|
from .regression.linear_model import OLS, GLS, WLS, GLSAR
|
||||||
|
from .genmod.generalized_linear_model import GLM
|
||||||
|
from .genmod import families
|
||||||
|
import robust
|
||||||
|
from .robust.robust_linear_model import RLM
|
||||||
|
from .discrete.discrete_model import Poisson, Logit, Probit, MNLogit
|
||||||
|
from .tsa import api as tsa
|
||||||
|
import nonparametric
|
||||||
|
import distributions
|
||||||
|
from __init__ import test
|
||||||
|
from . import version
|
||||||
|
from info import __doc__
|
||||||
|
from graphics.qqplot import qqplot
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
chmpath = os.path.join(os.path.dirname(__file__),
|
||||||
|
'docs\\build\\htmlhelp\\statsmodelsdoc.chm')
|
||||||
|
if os.path.exists(chmpath):
|
||||||
|
def open_help(chmpath=chmpath):
|
||||||
|
from subprocess import Popen
|
||||||
|
p = Popen(chmpath, shell=True)
|
||||||
|
|
||||||
|
|
||||||
|
del os
|
||||||
|
del chmpath
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,322 @@
|
|||||||
|
"""
|
||||||
|
Base tools for handling various kinds of data structures, attaching metadata to
|
||||||
|
results, and doing data cleaning
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from pandas import DataFrame, Series, TimeSeries
|
||||||
|
from scikits.statsmodels.tools.decorators import (resettable_cache,
|
||||||
|
cache_readonly, cache_writable)
|
||||||
|
import scikits.statsmodels.tools.data as data_util
|
||||||
|
|
||||||
|
class ModelData(object):
|
||||||
|
"""
|
||||||
|
Class responsible for handling input data and extracting metadata into the
|
||||||
|
appropriate form
|
||||||
|
"""
|
||||||
|
def __init__(self, endog, exog=None, **kwds):
|
||||||
|
self._orig_endog = endog
|
||||||
|
self._orig_exog = exog
|
||||||
|
self.endog, self.exog = self._convert_endog_exog(endog, exog)
|
||||||
|
self._check_integrity()
|
||||||
|
self._cache = resettable_cache()
|
||||||
|
|
||||||
|
def _convert_endog_exog(self, endog, exog):
|
||||||
|
|
||||||
|
# for consistent outputs if endog is (n,1)
|
||||||
|
yarr = self._get_yarr(endog)
|
||||||
|
xarr = None
|
||||||
|
if exog is not None:
|
||||||
|
xarr = self._get_xarr(exog)
|
||||||
|
if xarr.ndim == 1:
|
||||||
|
xarr = xarr[:, None]
|
||||||
|
if xarr.ndim != 2:
|
||||||
|
raise ValueError("exog is not 1d or 2d")
|
||||||
|
|
||||||
|
return yarr, xarr
|
||||||
|
|
||||||
|
@cache_writable()
|
||||||
|
def ynames(self):
|
||||||
|
endog = self._orig_endog
|
||||||
|
ynames = self._get_names(endog)
|
||||||
|
if not ynames:
|
||||||
|
ynames = _make_endog_names(endog)
|
||||||
|
|
||||||
|
if len(ynames) == 1:
|
||||||
|
return ynames[0]
|
||||||
|
else:
|
||||||
|
return list(ynames)
|
||||||
|
|
||||||
|
@cache_writable()
|
||||||
|
def xnames(self):
|
||||||
|
exog = self._orig_exog
|
||||||
|
if exog is not None:
|
||||||
|
xnames = self._get_names(exog)
|
||||||
|
if not xnames:
|
||||||
|
xnames = _make_exog_names(exog)
|
||||||
|
return list(xnames)
|
||||||
|
return None
|
||||||
|
|
||||||
|
@cache_readonly
|
||||||
|
def row_labels(self):
|
||||||
|
exog = self._orig_exog
|
||||||
|
if exog is not None:
|
||||||
|
row_labels = self._get_row_labels(exog)
|
||||||
|
else:
|
||||||
|
endog = self._orig_endog
|
||||||
|
row_labels = self._get_row_labels(endog)
|
||||||
|
return row_labels
|
||||||
|
|
||||||
|
def _get_row_labels(self, arr):
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _get_names(self, arr):
|
||||||
|
if isinstance(arr, DataFrame):
|
||||||
|
return list(arr.columns)
|
||||||
|
elif isinstance(arr, Series):
|
||||||
|
if arr.name:
|
||||||
|
return [arr.name]
|
||||||
|
else:
|
||||||
|
return
|
||||||
|
else:
|
||||||
|
try:
|
||||||
|
return arr.dtype.names
|
||||||
|
except AttributeError:
|
||||||
|
pass
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _get_yarr(self, endog):
|
||||||
|
if data_util.is_structured_ndarray(endog):
|
||||||
|
endog = data_util.struct_to_ndarray(endog)
|
||||||
|
return np.asarray(endog).squeeze()
|
||||||
|
|
||||||
|
def _get_xarr(self, exog):
|
||||||
|
if data_util.is_structured_ndarray(exog):
|
||||||
|
exog = data_util.struct_to_ndarray(exog)
|
||||||
|
return np.asarray(exog)
|
||||||
|
|
||||||
|
def _check_integrity(self):
|
||||||
|
if self.exog is not None:
|
||||||
|
if len(self.exog) != len(self.endog):
|
||||||
|
raise ValueError("endog and exog matrices are different sizes")
|
||||||
|
|
||||||
|
def wrap_output(self, obj, how='columns'):
|
||||||
|
if how == 'columns':
|
||||||
|
return self.attach_columns(obj)
|
||||||
|
elif how == 'rows':
|
||||||
|
return self.attach_rows(obj)
|
||||||
|
elif how == 'cov':
|
||||||
|
return self.attach_cov(obj)
|
||||||
|
elif how == 'dates':
|
||||||
|
return self.attach_dates(obj)
|
||||||
|
elif how == 'columns_eq':
|
||||||
|
return self.attach_columns_eq(obj)
|
||||||
|
elif how == 'cov_eq':
|
||||||
|
return self.attach_cov_eq(obj)
|
||||||
|
else:
|
||||||
|
return obj
|
||||||
|
|
||||||
|
def attach_columns(self, result):
|
||||||
|
return result
|
||||||
|
|
||||||
|
def attach_columns_eq(self, result):
|
||||||
|
return result
|
||||||
|
|
||||||
|
def attach_cov(self, result):
|
||||||
|
return result
|
||||||
|
|
||||||
|
def attach_cov_eq(self, result):
|
||||||
|
return result
|
||||||
|
|
||||||
|
def attach_rows(self, result):
|
||||||
|
return result
|
||||||
|
|
||||||
|
def attach_dates(self, result):
|
||||||
|
return result
|
||||||
|
|
||||||
|
class PandasData(ModelData):
|
||||||
|
"""
|
||||||
|
Data handling class which knows how to reattach pandas metadata to model
|
||||||
|
results
|
||||||
|
"""
|
||||||
|
|
||||||
|
def _get_row_labels(self, arr):
|
||||||
|
return arr.index
|
||||||
|
|
||||||
|
def attach_columns(self, result):
|
||||||
|
if result.squeeze().ndim == 1:
|
||||||
|
return Series(result, index=self.xnames)
|
||||||
|
else: # for e.g., confidence intervals
|
||||||
|
return DataFrame(result, index=self.xnames)
|
||||||
|
|
||||||
|
def attach_columns_eq(self, result):
|
||||||
|
return DataFrame(result, index=self.xnames, columns=self.ynames)
|
||||||
|
|
||||||
|
def attach_cov(self, result):
|
||||||
|
return DataFrame(result, index=self.xnames, columns=self.xnames)
|
||||||
|
|
||||||
|
def attach_cov_eq(self, result):
|
||||||
|
return DataFrame(result, index=self.ynames, columns=self.ynames)
|
||||||
|
|
||||||
|
def attach_rows(self, result):
|
||||||
|
# assumes if len(row_labels) > len(result) it's bc it was truncated
|
||||||
|
# at the front, for AR lags, for example
|
||||||
|
if result.squeeze().ndim == 1:
|
||||||
|
return Series(result, index=self.row_labels[-len(result):])
|
||||||
|
else: # this is for VAR results, may not be general enough
|
||||||
|
return DataFrame(result, index=self.row_labels[-len(result):],
|
||||||
|
columns=self.ynames)
|
||||||
|
|
||||||
|
def attach_dates(self, result):
|
||||||
|
return TimeSeries(result, index=self.predict_dates)
|
||||||
|
|
||||||
|
class TimeSeriesData(ModelData):
|
||||||
|
"""
|
||||||
|
Data handling class which returns scikits.timeseries model results
|
||||||
|
"""
|
||||||
|
def _get_row_labels(self, arr):
|
||||||
|
return arr.dates
|
||||||
|
|
||||||
|
#def attach_columns(self, result):
|
||||||
|
# return recarray?
|
||||||
|
|
||||||
|
#def attach_cov(self, result):
|
||||||
|
# return recarray?
|
||||||
|
|
||||||
|
def attach_rows(self, result):
|
||||||
|
from scikits.timeseries import time_series
|
||||||
|
return time_series(result, dates = self.row_labels[-len(result):])
|
||||||
|
|
||||||
|
def attach_dates(self, result):
|
||||||
|
from scikits.timeseries import time_series
|
||||||
|
return time_series(result, dates = self.predict_dates)
|
||||||
|
|
||||||
|
|
||||||
|
_la = None
|
||||||
|
def _lazy_import_larry():
|
||||||
|
global _la
|
||||||
|
import la
|
||||||
|
_la = la
|
||||||
|
|
||||||
|
|
||||||
|
class LarryData(ModelData):
|
||||||
|
"""
|
||||||
|
Data handling class which knows how to reattach pandas metadata to model
|
||||||
|
results
|
||||||
|
"""
|
||||||
|
def __init__(self, endog, exog=None, **kwds):
|
||||||
|
_lazy_import_larry()
|
||||||
|
super(LarryData, self).__init__(endog, exog=exog, **kwds)
|
||||||
|
|
||||||
|
def _get_yarr(self, endog):
|
||||||
|
try:
|
||||||
|
return endog.x
|
||||||
|
except AttributeError:
|
||||||
|
return np.asarray(endog).squeeze()
|
||||||
|
|
||||||
|
def _get_xarr(self, exog):
|
||||||
|
try:
|
||||||
|
return exog.x
|
||||||
|
except AttributeError:
|
||||||
|
return np.asarray(exog)
|
||||||
|
|
||||||
|
def _get_names(self, exog):
|
||||||
|
try:
|
||||||
|
return exog.label[1]
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _get_row_labels(self, arr):
|
||||||
|
return arr.label[0]
|
||||||
|
|
||||||
|
def attach_columns(self, result):
|
||||||
|
if result.ndim == 1:
|
||||||
|
return _la.larry(result, [self.xnames])
|
||||||
|
else:
|
||||||
|
shape = results.shape
|
||||||
|
return _la.larray(result, [self.xnames, range(shape[1])])
|
||||||
|
|
||||||
|
def attach_columns_eq(self, result):
|
||||||
|
return _la.larray(result, [self.xnames], [self.xnames])
|
||||||
|
|
||||||
|
def attach_cov(self, result):
|
||||||
|
return _la.larry(result, [self.xnames], [self.xnames])
|
||||||
|
|
||||||
|
def attach_cov_eq(self, result):
|
||||||
|
return _la.larray(result, [self.ynames], [self.ynames])
|
||||||
|
|
||||||
|
def attach_rows(self, result):
|
||||||
|
return _la.larry(result, [self.row_labels[-len(result):]])
|
||||||
|
|
||||||
|
def attach_dates(self, result):
|
||||||
|
return _la.larray(result, [self.predict_dates])
|
||||||
|
|
||||||
|
def _is_structured_array(data):
|
||||||
|
return isinstance(data, np.ndarray) and data.dtype.names is not None
|
||||||
|
|
||||||
|
def _make_endog_names(endog):
|
||||||
|
if endog.ndim == 1 or endog.shape[1] == 1:
|
||||||
|
ynames = ['y']
|
||||||
|
else: # for VAR
|
||||||
|
ynames = ['y%d' % (i+1) for i in range(endog.shape[1])]
|
||||||
|
|
||||||
|
return ynames
|
||||||
|
|
||||||
|
def _make_exog_names(exog):
|
||||||
|
exog_var = exog.var(0)
|
||||||
|
if (exog_var == 0).any():
|
||||||
|
# assumes one constant in first or last position
|
||||||
|
# avoid exception if more than one constant
|
||||||
|
const_idx = exog_var.argmin()
|
||||||
|
if const_idx == exog.shape[1] - 1:
|
||||||
|
exog_names = ['x%d' % i for i in range(1,exog.shape[1])]
|
||||||
|
exog_names += ['const']
|
||||||
|
else:
|
||||||
|
exog_names = ['x%d' % i for i in range(exog.shape[1])]
|
||||||
|
exog_names[const_idx] = 'const'
|
||||||
|
else:
|
||||||
|
exog_names = ['x%d' % i for i in range(exog.shape[1])]
|
||||||
|
|
||||||
|
return exog_names
|
||||||
|
|
||||||
|
def handle_data(endog, exog):
|
||||||
|
"""
|
||||||
|
Given inputs
|
||||||
|
"""
|
||||||
|
if _is_using_pandas(endog, exog):
|
||||||
|
klass = PandasData
|
||||||
|
elif _is_using_larry(endog, exog):
|
||||||
|
klass = LarryData
|
||||||
|
elif _is_using_timeseries(endog, exog):
|
||||||
|
klass = TimeSeriesData
|
||||||
|
# keep this check last
|
||||||
|
elif _is_using_ndarray(endog, exog):
|
||||||
|
klass = ModelData
|
||||||
|
else:
|
||||||
|
raise ValueError('unrecognized data structures: %s / %s' %
|
||||||
|
(type(endog), type(exog)))
|
||||||
|
|
||||||
|
return klass(endog, exog=exog)
|
||||||
|
|
||||||
|
def _is_using_ndarray(endog, exog):
|
||||||
|
return (isinstance(endog, np.ndarray) and
|
||||||
|
(isinstance(exog, np.ndarray) or exog is None))
|
||||||
|
|
||||||
|
def _is_using_pandas(endog, exog):
|
||||||
|
from pandas import Series, DataFrame, WidePanel
|
||||||
|
klasses = (Series, DataFrame, WidePanel)
|
||||||
|
return (isinstance(endog, klasses) or isinstance(exog, klasses))
|
||||||
|
|
||||||
|
def _is_using_larry(endog, exog):
|
||||||
|
try:
|
||||||
|
import la
|
||||||
|
return isinstance(endog, la.larry) or isinstance(exog, la.larry)
|
||||||
|
except ImportError:
|
||||||
|
return False
|
||||||
|
|
||||||
|
def _is_using_timeseries(endog, exog):
|
||||||
|
from scikits.timeseries import TimeSeries as tsTimeSeries
|
||||||
|
return isinstance(endog, tsTimeSeries) or isinstance(exog, tsTimeSeries)
|
||||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,101 @@
|
|||||||
|
import inspect
|
||||||
|
import functools
|
||||||
|
import types
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
class ResultsWrapper(object):
|
||||||
|
"""
|
||||||
|
Class which wraps a statsmodels estimation Results class and steps in to
|
||||||
|
reattach metadata to results (if available)
|
||||||
|
"""
|
||||||
|
_wrap_attrs = {}
|
||||||
|
_wrap_methods = {}
|
||||||
|
|
||||||
|
def __init__(self, results):
|
||||||
|
self._results = results
|
||||||
|
self.__doc__ = results.__doc__
|
||||||
|
|
||||||
|
def __dir__(self):
|
||||||
|
return [x for x in dir(self._results)]
|
||||||
|
|
||||||
|
def __getattribute__(self, attr):
|
||||||
|
get = lambda name: object.__getattribute__(self, name)
|
||||||
|
results = get('_results')
|
||||||
|
|
||||||
|
try:
|
||||||
|
return get(attr)
|
||||||
|
except AttributeError:
|
||||||
|
pass
|
||||||
|
|
||||||
|
obj = getattr(results, attr)
|
||||||
|
data = results.model._data
|
||||||
|
how = self._wrap_attrs.get(attr)
|
||||||
|
if how:
|
||||||
|
obj = data.wrap_output(obj, how=how)
|
||||||
|
|
||||||
|
return obj
|
||||||
|
|
||||||
|
def union_dicts(*dicts):
|
||||||
|
result = {}
|
||||||
|
for d in dicts:
|
||||||
|
result.update(d)
|
||||||
|
return result
|
||||||
|
|
||||||
|
def make_wrapper(func, how):
|
||||||
|
@functools.wraps(func)
|
||||||
|
def wrapper(self, *args, **kwargs):
|
||||||
|
results = object.__getattribute__(self, '_results')
|
||||||
|
data = results.model._data
|
||||||
|
return data.wrap_output(func(results, *args, **kwargs), how)
|
||||||
|
|
||||||
|
argspec = inspect.getargspec(func)
|
||||||
|
formatted = inspect.formatargspec(argspec.args, varargs=argspec.varargs,
|
||||||
|
defaults=argspec.defaults)
|
||||||
|
|
||||||
|
wrapper.__doc__ = "%s%s\n%s" % (func.im_func.func_name, formatted,
|
||||||
|
wrapper.__doc__)
|
||||||
|
|
||||||
|
return wrapper
|
||||||
|
|
||||||
|
def populate_wrapper(klass, wrapping):
|
||||||
|
for meth, how in klass._wrap_methods.iteritems():
|
||||||
|
if not hasattr(wrapping, meth):
|
||||||
|
continue
|
||||||
|
|
||||||
|
func = getattr(wrapping, meth)
|
||||||
|
wrapper = make_wrapper(func, how)
|
||||||
|
setattr(klass, meth, wrapper)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
import scikits.statsmodels.api as sm
|
||||||
|
from pandas import DataFrame
|
||||||
|
data = sm.datasets.longley.load()
|
||||||
|
df = DataFrame(data.exog, columns=data.exog_name)
|
||||||
|
y = data.endog
|
||||||
|
# data.exog = sm.add_constant(data.exog)
|
||||||
|
df['intercept'] = 1.
|
||||||
|
olsresult = sm.OLS(y, df).fit()
|
||||||
|
rlmresult = sm.RLM(y, df).fit()
|
||||||
|
|
||||||
|
# olswrap = RegressionResultsWrapper(olsresult)
|
||||||
|
# rlmwrap = RLMResultsWrapper(rlmresult)
|
||||||
|
|
||||||
|
data = sm.datasets.wfs.load()
|
||||||
|
# get offset
|
||||||
|
offset = np.log(data.exog[:,-1])
|
||||||
|
exog = data.exog[:,:-1]
|
||||||
|
|
||||||
|
# convert dur to dummy
|
||||||
|
exog = sm.tools.categorical(exog, col=0, drop=True)
|
||||||
|
# drop reference category
|
||||||
|
# convert res to dummy
|
||||||
|
exog = sm.tools.categorical(exog, col=0, drop=True)
|
||||||
|
# convert edu to dummy
|
||||||
|
exog = sm.tools.categorical(exog, col=0, drop=True)
|
||||||
|
# drop reference categories and add intercept
|
||||||
|
exog = sm.add_constant(exog[:,[1,2,3,4,5,7,8,10,11,12]])
|
||||||
|
|
||||||
|
endog = np.round(data.endog)
|
||||||
|
mod = sm.GLM(endog, exog, family=sm.families.Poisson()).fit()
|
||||||
|
# glmwrap = GLMResultsWrapper(mod)
|
||||||
@ -0,0 +1,58 @@
|
|||||||
|
"""
|
||||||
|
Python 3 compatibility tools.
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
|
||||||
|
'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
|
||||||
|
'asstr', 'open_latin1']
|
||||||
|
|
||||||
|
import sys
|
||||||
|
|
||||||
|
if sys.version_info[0] >= 3:
|
||||||
|
import io
|
||||||
|
bytes = bytes
|
||||||
|
unicode = str
|
||||||
|
asunicode = str
|
||||||
|
def asbytes(s):
|
||||||
|
if isinstance(s, bytes):
|
||||||
|
return s
|
||||||
|
return s.encode('latin1')
|
||||||
|
def asstr(s):
|
||||||
|
if isinstance(s, str):
|
||||||
|
return s
|
||||||
|
return s.decode('latin1')
|
||||||
|
def isfileobj(f):
|
||||||
|
return isinstance(f, io.FileIO)
|
||||||
|
def open_latin1(filename, mode='r'):
|
||||||
|
return open(filename, mode=mode, encoding='iso-8859-1')
|
||||||
|
strchar = 'U'
|
||||||
|
else:
|
||||||
|
bytes = str
|
||||||
|
unicode = unicode
|
||||||
|
asbytes = str
|
||||||
|
asstr = str
|
||||||
|
strchar = 'S'
|
||||||
|
def isfileobj(f):
|
||||||
|
return isinstance(f, file)
|
||||||
|
def asunicode(s):
|
||||||
|
if isinstance(s, unicode):
|
||||||
|
return s
|
||||||
|
return s.decode('ascii')
|
||||||
|
def open_latin1(filename, mode='r'):
|
||||||
|
return open(filename, mode=mode)
|
||||||
|
|
||||||
|
def getexception():
|
||||||
|
return sys.exc_info()[1]
|
||||||
|
|
||||||
|
def asbytes_nested(x):
|
||||||
|
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
|
||||||
|
return [asbytes_nested(y) for y in x]
|
||||||
|
else:
|
||||||
|
return asbytes(x)
|
||||||
|
|
||||||
|
def asunicode_nested(x):
|
||||||
|
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
|
||||||
|
return [asunicode_nested(y) for y in x]
|
||||||
|
else:
|
||||||
|
return asunicode(x)
|
||||||
@ -0,0 +1,35 @@
|
|||||||
|
Last Change: Tue Jul 17 05:00 PM 2007 J
|
||||||
|
|
||||||
|
The code and descriptive text is copyrighted and offered under the terms of
|
||||||
|
the BSD License from the authors; see below. However, the actual dataset may
|
||||||
|
have a different origin and intellectual property status. See the SOURCE and
|
||||||
|
COPYRIGHT variables for this information.
|
||||||
|
|
||||||
|
Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
|
||||||
|
All rights reserved.
|
||||||
|
|
||||||
|
Redistribution and use in source and binary forms, with or without
|
||||||
|
modification, are permitted provided that the following conditions are
|
||||||
|
met:
|
||||||
|
|
||||||
|
* Redistributions of source code must retain the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer.
|
||||||
|
* Redistributions in binary form must reproduce the above copyright
|
||||||
|
notice, this list of conditions and the following disclaimer in
|
||||||
|
the documentation and/or other materials provided with the
|
||||||
|
distribution.
|
||||||
|
* Neither the author nor the names of any contributors may be used
|
||||||
|
to endorse or promote products derived from this software without
|
||||||
|
specific prior written permission.
|
||||||
|
|
||||||
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||||
|
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
|
||||||
|
TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||||
|
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||||
|
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||||
|
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||||
|
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
|
||||||
|
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
|
||||||
|
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
|
||||||
|
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
|
||||||
|
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||||
@ -0,0 +1,32 @@
|
|||||||
|
This README was copied from
|
||||||
|
http://projects.scipy.org/scikits/browser/trunk/learn/scikits/learn/datasets/
|
||||||
|
-----------------------------------------------------------------------------
|
||||||
|
|
||||||
|
Last Change: Tue Jul 17 04:00 PM 2007 J
|
||||||
|
|
||||||
|
This packages datasets defines a set of packages which contain datasets useful
|
||||||
|
for demo, examples, etc... This can be seen as an equivalent of the R dataset
|
||||||
|
package, but for python.
|
||||||
|
|
||||||
|
Each subdir is a python package, and should define the function load, returning
|
||||||
|
the corresponding data. For example, to access datasets data1, you should be able to do:
|
||||||
|
|
||||||
|
>> from datasets.data1 import load
|
||||||
|
>> d = load() # -> d contains the data of the datasets data1
|
||||||
|
|
||||||
|
load can do whatever it wants: fetching data from a file (python script, csv
|
||||||
|
file, etc...), from the internet, etc... Some special variables must be defined
|
||||||
|
for each package, containing a python string:
|
||||||
|
- COPYRIGHT: copyright informations
|
||||||
|
- SOURCE: where the data are coming from
|
||||||
|
- DESCHOSRT: short description
|
||||||
|
- DESCLONG: long description
|
||||||
|
- NOTE: some notes on the datasets.
|
||||||
|
|
||||||
|
For the datasets to be useful in the learn scikits, which is the project which initiated this datasets package, the data returned by load has to be a dict with the following conventions:
|
||||||
|
- 'data': this value should be a record array containing the actual data.
|
||||||
|
- 'label': this value should be a rank 1 array of integers, contains the
|
||||||
|
label index for each sample, that is label[i] should be the label index
|
||||||
|
of data[i].
|
||||||
|
- 'class': a record array such as class[i] is the class name. In other
|
||||||
|
words, this makes the correspondance label index <> label name.
|
||||||
@ -0,0 +1,8 @@
|
|||||||
|
"""
|
||||||
|
Datasets module
|
||||||
|
"""
|
||||||
|
#__all__ = filter(lambda s:not s.startswith('_'),dir())
|
||||||
|
import anes96, committee, ccard, copper, cpunish, grunfeld, longley, \
|
||||||
|
macrodata, randhie, scotland, spector, stackloss, star98, sunspots, \
|
||||||
|
nile, strikes
|
||||||
|
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,945 @@
|
|||||||
|
'popul' 'TVnews' 'selfLR' 'ClinLR' 'DoleLR' 'PID' 'age' 'educ' 'income' 'vote'
|
||||||
|
0 7 7 1 6 6 36 3 1 1
|
||||||
|
190 1 3 3 5 1 20 4 1 0
|
||||||
|
31 7 2 2 6 1 24 6 1 0
|
||||||
|
83 4 3 4 5 1 28 6 1 0
|
||||||
|
640 7 5 6 4 0 68 6 1 0
|
||||||
|
110 3 3 4 6 1 21 4 1 0
|
||||||
|
100 7 5 6 4 1 77 4 1 0
|
||||||
|
31 1 5 4 5 4 21 4 1 0
|
||||||
|
180 7 4 6 3 3 31 4 1 0
|
||||||
|
2800 0 3 3 7 0 39 3 1 0
|
||||||
|
1600 0 3 2 4 4 26 2 1 0
|
||||||
|
330 5 4 3 6 1 31 4 1 0
|
||||||
|
190 2 5 4 6 5 22 4 1 1
|
||||||
|
100 7 4 4 6 0 42 5 1 0
|
||||||
|
1000 7 5 7 4 0 74 1 1 0
|
||||||
|
0 7 6 7 5 0 62 3 1 0
|
||||||
|
130 7 4 4 5 1 58 3 1 0
|
||||||
|
5 5 3 3 6 1 24 6 1 0
|
||||||
|
33 7 6 2 6 5 51 4 1 1
|
||||||
|
19 2 2 1 4 0 36 3 2 0
|
||||||
|
74 7 4 4 7 2 88 2 2 0
|
||||||
|
190 0 2 4 6 2 20 4 2 0
|
||||||
|
12 3 4 6 3 2 27 3 2 0
|
||||||
|
0 7 6 1 6 6 44 4 2 1
|
||||||
|
19 0 4 2 2 1 45 3 2 0
|
||||||
|
0 2 4 3 6 1 21 4 2 0
|
||||||
|
390 5 3 4 7 1 40 5 2 0
|
||||||
|
40 7 4 3 4 0 40 6 2 0
|
||||||
|
3 3 5 5 4 1 48 3 2 0
|
||||||
|
450 3 4 7 1 0 34 3 2 0
|
||||||
|
350 0 3 4 7 2 26 2 2 0
|
||||||
|
64 3 4 4 2 1 60 2 3 0
|
||||||
|
3 0 4 4 3 0 32 3 3 0
|
||||||
|
0 1 4 3 7 1 31 3 3 0
|
||||||
|
640 7 7 5 7 4 33 3 3 1
|
||||||
|
0 7 3 4 6 0 57 3 3 0
|
||||||
|
12 7 4 3 6 1 84 3 3 0
|
||||||
|
62 6 7 2 7 5 75 3 3 1
|
||||||
|
31 2 7 2 6 6 19 4 3 1
|
||||||
|
0 1 3 2 6 1 47 6 3 0
|
||||||
|
180 6 5 5 5 0 51 2 3 0
|
||||||
|
640 3 6 4 4 5 40 3 3 0
|
||||||
|
110 0 2 3 6 1 22 6 3 0
|
||||||
|
100 1 7 7 5 6 35 2 3 0
|
||||||
|
100 7 4 4 7 2 43 5 3 0
|
||||||
|
11 3 6 6 3 2 76 6 3 0
|
||||||
|
0 7 4 3 1 6 45 3 3 1
|
||||||
|
4 7 4 6 6 0 88 2 3 0
|
||||||
|
35 6 4 4 2 1 46 3 4 0
|
||||||
|
0 1 3 4 5 2 22 6 4 0
|
||||||
|
0 7 5 1 6 5 68 3 4 1
|
||||||
|
0 2 5 2 6 5 38 3 4 1
|
||||||
|
33 7 4 3 6 3 69 2 4 0
|
||||||
|
270 2 5 4 3 0 67 3 4 0
|
||||||
|
45 7 2 4 6 0 88 4 4 0
|
||||||
|
40 3 6 2 5 5 68 3 4 1
|
||||||
|
6 1 5 2 4 2 76 3 4 1
|
||||||
|
2 7 4 4 6 0 72 2 4 0
|
||||||
|
0 0 6 2 6 6 37 6 4 1
|
||||||
|
35 3 4 2 6 0 69 3 4 0
|
||||||
|
83 0 2 4 6 0 33 6 4 0
|
||||||
|
3500 7 2 2 6 0 34 4 4 0
|
||||||
|
100 2 4 4 7 2 30 3 4 0
|
||||||
|
350 2 3 3 6 1 19 3 4 0
|
||||||
|
100 3 4 6 2 0 44 3 4 0
|
||||||
|
67 1 4 4 7 1 64 3 4 0
|
||||||
|
30 5 7 7 2 0 37 4 4 0
|
||||||
|
0 7 6 3 5 4 31 5 5 1
|
||||||
|
0 0 6 1 5 4 88 4 5 1
|
||||||
|
6 7 6 2 6 6 77 4 5 1
|
||||||
|
350 1 4 5 6 5 30 6 5 0
|
||||||
|
400 1 2 3 7 1 32 4 5 0
|
||||||
|
15 7 6 2 6 6 59 1 5 1
|
||||||
|
0 0 4 4 4 3 47 4 5 0
|
||||||
|
3 2 4 6 5 1 22 3 5 0
|
||||||
|
22 5 4 2 6 2 55 3 5 0
|
||||||
|
64 2 2 1 3 0 24 2 5 0
|
||||||
|
32 5 3 7 4 1 65 1 5 0
|
||||||
|
390 7 3 6 2 2 24 3 5 0
|
||||||
|
0 7 3 4 5 3 30 3 5 0
|
||||||
|
0 7 4 5 2 3 73 3 5 0
|
||||||
|
59 5 3 3 5 1 73 5 5 0
|
||||||
|
0 6 4 3 6 2 91 1 5 0
|
||||||
|
35 7 3 2 5 0 71 2 5 0
|
||||||
|
0 2 6 4 5 4 34 4 5 1
|
||||||
|
170 7 4 3 2 0 48 2 6 0
|
||||||
|
12 1 6 2 6 5 42 4 6 1
|
||||||
|
40 4 6 5 4 0 72 2 6 0
|
||||||
|
31 2 3 4 6 6 20 4 6 1
|
||||||
|
31 7 2 2 7 0 22 4 6 0
|
||||||
|
1600 1 3 3 6 1 24 6 6 0
|
||||||
|
1 1 4 2 7 2 39 6 6 0
|
||||||
|
4 7 6 1 6 6 83 5 6 1
|
||||||
|
190 0 6 2 6 6 39 3 6 1
|
||||||
|
53 3 5 3 6 1 33 5 6 0
|
||||||
|
31 7 4 3 6 1 53 3 6 1
|
||||||
|
16 7 5 3 6 5 82 3 6 1
|
||||||
|
33 5 4 3 5 6 82 3 6 1
|
||||||
|
0 3 5 3 6 5 47 6 7 1
|
||||||
|
0 3 4 2 7 4 68 3 7 0
|
||||||
|
0 7 4 3 5 0 84 6 7 0
|
||||||
|
27 2 6 1 6 5 35 5 7 1
|
||||||
|
84 7 4 5 6 1 67 2 7 0
|
||||||
|
22 3 5 3 5 4 33 2 7 1
|
||||||
|
0 3 3 3 5 0 49 7 7 0
|
||||||
|
3500 0 4 3 7 0 91 1 7 0
|
||||||
|
390 7 4 5 3 1 43 3 7 0
|
||||||
|
0 7 4 3 2 6 65 4 7 0
|
||||||
|
16 7 5 6 3 0 69 3 7 0
|
||||||
|
200 0 5 5 4 1 56 4 8 0
|
||||||
|
640 0 2 3 5 0 24 6 8 0
|
||||||
|
0 7 4 4 5 0 77 3 8 0
|
||||||
|
45 7 6 3 7 0 74 3 8 0
|
||||||
|
12 0 7 3 6 6 25 6 8 1
|
||||||
|
20 7 6 2 5 4 85 1 8 1
|
||||||
|
7300 5 7 7 6 3 21 2 8 0
|
||||||
|
64 7 6 3 1 0 24 4 8 0
|
||||||
|
13 7 5 4 7 4 73 4 8 0
|
||||||
|
190 0 4 5 3 2 37 3 8 0
|
||||||
|
9 4 4 5 1 2 35 4 8 0
|
||||||
|
0 7 4 4 7 0 47 3 8 0
|
||||||
|
170 2 4 2 6 6 21 3 8 1
|
||||||
|
640 7 3 6 4 0 55 5 8 0
|
||||||
|
9 4 6 3 6 6 30 6 8 1
|
||||||
|
0 4 5 3 6 4 76 7 8 1
|
||||||
|
7300 5 3 4 3 3 36 4 8 0
|
||||||
|
2800 0 1 1 7 0 38 3 9 0
|
||||||
|
0 7 2 3 5 0 67 3 9 0
|
||||||
|
30 7 7 3 7 6 70 2 9 1
|
||||||
|
44 7 5 3 7 2 78 4 9 0
|
||||||
|
7300 1 2 2 7 3 27 6 9 0
|
||||||
|
330 4 3 5 6 1 51 4 9 0
|
||||||
|
3 0 6 7 3 5 33 4 9 0
|
||||||
|
51 2 6 1 5 6 80 6 9 1
|
||||||
|
29 5 4 1 6 1 79 1 9 0
|
||||||
|
630 2 6 4 5 4 66 1 9 1
|
||||||
|
170 0 4 1 6 0 32 3 10 0
|
||||||
|
33 7 4 5 7 0 70 2 10 0
|
||||||
|
0 3 2 3 6 3 42 3 10 0
|
||||||
|
9 5 5 4 5 5 73 4 10 1
|
||||||
|
22 4 4 4 6 0 87 2 10 0
|
||||||
|
100 0 7 5 1 1 30 5 10 0
|
||||||
|
2 2 4 4 5 3 52 3 10 0
|
||||||
|
0 6 5 3 6 1 62 4 10 0
|
||||||
|
50 7 6 3 4 0 67 3 10 0
|
||||||
|
15 4 6 3 4 4 37 6 10 0
|
||||||
|
3 4 3 5 7 0 37 4 10 0
|
||||||
|
720 5 1 5 6 1 64 6 10 0
|
||||||
|
640 7 1 1 5 0 34 3 10 0
|
||||||
|
5 7 4 4 7 0 70 3 10 0
|
||||||
|
24 2 6 2 6 6 31 5 10 1
|
||||||
|
22 7 2 2 6 0 29 6 11 0
|
||||||
|
55 7 4 5 4 1 71 2 11 0
|
||||||
|
0 2 4 4 4 0 67 1 11 0
|
||||||
|
1600 5 4 4 6 0 41 7 11 0
|
||||||
|
170 6 1 2 6 0 49 6 11 0
|
||||||
|
1000 7 4 4 5 0 42 5 11 0
|
||||||
|
63 0 6 3 2 0 78 2 11 0
|
||||||
|
110 0 4 1 6 1 24 3 11 0
|
||||||
|
16 7 4 6 6 1 29 3 11 0
|
||||||
|
100 3 4 2 6 5 39 5 11 1
|
||||||
|
7300 3 5 3 6 1 19 4 11 0
|
||||||
|
22 2 4 2 7 1 32 5 11 0
|
||||||
|
71 3 4 2 6 5 69 3 11 1
|
||||||
|
900 4 5 2 5 5 83 3 11 1
|
||||||
|
35 7 4 1 5 4 76 2 11 1
|
||||||
|
2 7 7 1 2 0 62 2 11 0
|
||||||
|
83 2 3 3 6 0 47 7 11 0
|
||||||
|
370 5 6 7 4 0 35 3 11 0
|
||||||
|
12 0 4 5 3 4 23 3 11 0
|
||||||
|
370 7 4 4 1 1 79 4 11 0
|
||||||
|
100 7 6 2 6 5 64 5 11 1
|
||||||
|
470 7 6 2 4 5 70 4 11 1
|
||||||
|
22 7 6 1 6 6 87 5 11 1
|
||||||
|
2800 0 3 6 1 0 28 2 12 0
|
||||||
|
47 5 3 5 7 1 58 3 12 0
|
||||||
|
900 5 4 4 6 1 85 2 12 0
|
||||||
|
330 7 3 6 4 0 62 3 12 0
|
||||||
|
84 0 3 2 7 1 26 6 12 0
|
||||||
|
0 0 6 2 5 5 28 3 12 1
|
||||||
|
33 3 6 1 7 6 88 2 12 1
|
||||||
|
53 7 2 3 6 0 57 6 12 0
|
||||||
|
8 7 2 2 6 0 78 3 12 0
|
||||||
|
2 7 4 4 2 0 56 3 12 0
|
||||||
|
0 0 4 6 3 3 46 5 12 0
|
||||||
|
0 2 4 4 3 5 20 3 12 1
|
||||||
|
0 0 5 6 4 1 24 4 12 0
|
||||||
|
0 7 5 2 6 2 72 4 12 0
|
||||||
|
15 7 2 4 7 1 51 4 12 0
|
||||||
|
900 0 6 2 5 6 34 6 12 1
|
||||||
|
30 2 4 2 6 1 21 4 12 0
|
||||||
|
0 7 4 4 6 2 74 7 12 0
|
||||||
|
170 3 4 4 6 1 48 1 12 0
|
||||||
|
900 2 3 3 7 5 28 3 12 0
|
||||||
|
0 6 7 1 7 5 38 2 12 1
|
||||||
|
1600 7 4 6 1 0 70 3 12 0
|
||||||
|
0 7 4 5 4 0 72 2 12 0
|
||||||
|
2800 0 4 5 6 0 41 3 12 0
|
||||||
|
110 5 3 4 5 1 50 7 12 0
|
||||||
|
1 7 6 2 5 5 73 3 12 1
|
||||||
|
3 5 5 2 4 0 79 6 12 0
|
||||||
|
0 4 5 1 4 5 76 2 12 1
|
||||||
|
22 0 5 3 5 5 62 5 12 1
|
||||||
|
63 3 6 2 6 6 30 6 12 1
|
||||||
|
290 0 6 3 6 5 35 4 12 1
|
||||||
|
2 7 1 2 7 1 66 4 12 0
|
||||||
|
40 0 2 4 6 0 35 4 12 0
|
||||||
|
67 0 6 1 5 6 57 6 12 1
|
||||||
|
0 5 4 5 4 5 37 5 12 1
|
||||||
|
470 7 5 5 2 1 61 3 13 0
|
||||||
|
0 7 6 2 6 6 56 3 13 1
|
||||||
|
4 6 3 4 5 1 53 3 13 0
|
||||||
|
20 0 4 5 3 2 24 6 13 1
|
||||||
|
2800 7 4 1 6 5 74 3 13 1
|
||||||
|
0 0 4 4 3 1 36 3 13 0
|
||||||
|
1 0 6 2 4 5 30 5 13 1
|
||||||
|
640 0 4 7 4 1 55 2 13 0
|
||||||
|
170 3 3 2 7 2 35 6 13 0
|
||||||
|
270 2 3 4 6 0 26 4 13 0
|
||||||
|
390 0 3 4 6 2 25 4 13 0
|
||||||
|
16 2 6 7 4 3 27 3 13 0
|
||||||
|
11 7 4 1 6 5 66 3 13 1
|
||||||
|
0 1 5 2 6 2 39 2 13 0
|
||||||
|
270 7 1 1 2 2 58 5 13 0
|
||||||
|
170 2 4 4 4 0 53 3 13 1
|
||||||
|
900 7 6 7 4 0 76 3 13 0
|
||||||
|
270 7 5 2 7 1 51 3 13 0
|
||||||
|
0 7 4 2 7 0 70 2 13 0
|
||||||
|
350 3 6 3 6 6 68 4 13 1
|
||||||
|
0 0 5 4 5 2 32 3 13 1
|
||||||
|
6 0 5 4 5 5 55 3 13 0
|
||||||
|
290 7 2 2 6 0 52 4 13 0
|
||||||
|
630 7 6 4 6 4 73 2 13 1
|
||||||
|
900 0 5 4 7 0 42 2 13 0
|
||||||
|
31 2 4 4 3 4 23 5 13 1
|
||||||
|
1600 5 2 3 6 0 30 7 14 0
|
||||||
|
71 7 2 2 7 0 68 4 14 0
|
||||||
|
200 7 5 2 3 2 68 3 14 0
|
||||||
|
0 0 6 4 7 3 68 6 14 0
|
||||||
|
30 5 2 3 6 0 38 5 14 0
|
||||||
|
10 1 4 3 6 2 74 3 14 0
|
||||||
|
0 7 5 6 3 0 59 2 14 0
|
||||||
|
900 2 5 2 5 2 73 2 14 0
|
||||||
|
71 7 2 3 6 0 79 3 14 0
|
||||||
|
22 3 7 1 6 5 28 4 14 1
|
||||||
|
0 7 6 2 6 6 50 3 14 1
|
||||||
|
0 4 6 3 1 1 36 4 14 0
|
||||||
|
0 3 6 2 6 6 50 3 14 1
|
||||||
|
0 1 6 2 6 6 61 3 14 1
|
||||||
|
7300 3 2 2 6 0 37 4 14 0
|
||||||
|
83 0 3 4 7 0 29 6 14 0
|
||||||
|
93 7 2 3 7 0 39 4 14 0
|
||||||
|
0 7 4 5 4 2 83 6 14 1
|
||||||
|
51 7 6 1 5 4 68 6 14 1
|
||||||
|
31 2 6 1 5 6 25 4 14 1
|
||||||
|
93 2 1 3 6 1 41 6 14 0
|
||||||
|
0 7 3 2 6 1 67 3 14 0
|
||||||
|
0 3 4 2 6 4 36 6 14 1
|
||||||
|
31 4 6 2 6 4 66 4 14 1
|
||||||
|
900 1 3 2 7 1 55 4 14 0
|
||||||
|
0 4 3 2 6 2 42 5 14 0
|
||||||
|
2 7 6 3 5 5 42 3 14 1
|
||||||
|
110 3 4 5 7 1 36 3 14 0
|
||||||
|
63 1 6 4 6 4 53 5 14 1
|
||||||
|
900 0 3 2 5 1 36 5 14 0
|
||||||
|
31 3 4 3 6 2 29 7 14 0
|
||||||
|
510 1 4 4 6 0 31 3 14 0
|
||||||
|
270 2 3 4 6 1 43 6 14 0
|
||||||
|
9 3 3 4 7 1 33 6 14 0
|
||||||
|
3 1 6 6 2 0 63 3 14 0
|
||||||
|
29 1 5 2 4 2 25 5 14 0
|
||||||
|
45 2 3 2 6 0 72 4 14 0
|
||||||
|
83 5 3 3 6 1 40 4 14 0
|
||||||
|
22 7 4 2 6 2 27 6 14 0
|
||||||
|
15 3 5 4 7 2 26 4 15 0
|
||||||
|
110 7 5 2 5 6 67 3 15 1
|
||||||
|
8 0 4 5 5 1 21 3 15 0
|
||||||
|
11 5 6 2 6 6 27 7 15 1
|
||||||
|
56 4 6 5 3 0 78 6 15 1
|
||||||
|
8 3 4 4 3 1 32 3 15 0
|
||||||
|
100 5 4 2 6 1 68 4 15 0
|
||||||
|
900 7 4 2 6 5 76 4 15 0
|
||||||
|
67 7 3 3 6 0 33 5 15 0
|
||||||
|
35 4 6 2 5 4 38 2 15 1
|
||||||
|
35 3 4 3 5 1 49 7 15 0
|
||||||
|
22 0 5 4 6 5 61 4 15 1
|
||||||
|
110 7 7 1 5 4 57 3 15 1
|
||||||
|
12 0 4 2 5 2 20 3 15 0
|
||||||
|
7300 7 4 2 4 4 63 6 15 1
|
||||||
|
0 0 4 1 5 4 53 3 15 1
|
||||||
|
19 5 4 2 6 5 35 4 15 1
|
||||||
|
470 3 4 4 7 0 39 3 15 0
|
||||||
|
4 3 3 5 3 4 48 3 15 1
|
||||||
|
640 4 6 2 5 4 62 3 15 1
|
||||||
|
640 2 4 2 3 1 30 5 15 0
|
||||||
|
200 7 7 1 4 6 26 6 15 1
|
||||||
|
0 7 3 2 4 5 74 6 15 1
|
||||||
|
29 4 3 4 6 2 37 5 15 0
|
||||||
|
330 2 4 5 5 1 43 5 15 0
|
||||||
|
19 5 5 1 4 6 68 3 15 1
|
||||||
|
1 7 5 3 4 5 73 5 15 1
|
||||||
|
110 5 6 1 6 6 60 7 15 1
|
||||||
|
0 7 5 4 6 1 35 3 15 0
|
||||||
|
350 4 3 4 6 0 29 6 15 0
|
||||||
|
2 5 3 3 5 1 25 6 15 0
|
||||||
|
0 7 2 2 6 0 25 7 15 0
|
||||||
|
7 4 2 4 5 0 70 6 15 0
|
||||||
|
71 1 6 2 5 6 41 3 15 1
|
||||||
|
53 0 4 2 6 1 37 6 15 0
|
||||||
|
0 0 6 1 6 6 39 5 15 1
|
||||||
|
2 6 3 4 6 0 35 4 15 0
|
||||||
|
190 4 2 3 6 0 62 7 15 0
|
||||||
|
31 0 3 2 6 1 30 7 15 0
|
||||||
|
16 7 4 2 6 0 74 4 15 0
|
||||||
|
22 7 3 3 4 5 47 3 15 0
|
||||||
|
3 4 4 5 3 1 43 6 15 0
|
||||||
|
0 6 5 3 6 5 64 2 15 1
|
||||||
|
0 7 4 3 5 2 75 4 15 1
|
||||||
|
67 3 4 4 7 1 27 6 15 0
|
||||||
|
40 7 4 4 6 0 21 3 15 0
|
||||||
|
74 4 2 2 6 1 70 2 15 0
|
||||||
|
3 6 5 2 6 5 67 3 15 1
|
||||||
|
140 7 6 4 5 6 82 5 15 0
|
||||||
|
14 0 2 2 6 0 40 6 15 0
|
||||||
|
110 0 5 3 6 5 26 4 15 1
|
||||||
|
35 3 4 3 5 1 29 6 15 0
|
||||||
|
0 1 4 5 6 2 28 6 15 0
|
||||||
|
310 7 6 4 3 5 65 3 15 0
|
||||||
|
900 2 6 5 3 1 25 3 15 0
|
||||||
|
0 7 3 2 7 1 65 2 15 0
|
||||||
|
11 4 6 2 6 5 38 5 15 1
|
||||||
|
0 2 4 3 5 5 72 7 15 1
|
||||||
|
270 7 3 2 7 1 67 3 15 0
|
||||||
|
51 7 3 1 7 0 74 3 15 0
|
||||||
|
11 5 2 4 4 2 71 6 15 0
|
||||||
|
2 0 4 6 4 0 47 3 15 0
|
||||||
|
20 7 5 4 6 1 69 1 15 0
|
||||||
|
31 3 3 3 6 1 29 6 15 0
|
||||||
|
2 0 6 2 5 6 34 3 15 1
|
||||||
|
5 3 4 3 6 0 43 4 15 0
|
||||||
|
22 7 2 1 7 0 30 3 15 0
|
||||||
|
0 7 5 5 4 0 76 2 15 0
|
||||||
|
27 0 2 4 6 1 26 5 16 0
|
||||||
|
7 7 4 3 6 2 76 5 16 0
|
||||||
|
0 0 1 4 6 1 42 7 16 0
|
||||||
|
0 4 3 1 4 5 33 3 16 1
|
||||||
|
0 1 6 2 5 5 25 3 16 1
|
||||||
|
2800 0 2 2 7 0 51 4 16 0
|
||||||
|
0 4 4 2 5 4 57 3 16 1
|
||||||
|
22 1 6 1 5 6 21 4 16 1
|
||||||
|
9 7 5 1 4 5 79 7 16 1
|
||||||
|
0 1 6 2 5 5 35 5 16 1
|
||||||
|
9 5 4 2 5 4 57 6 16 1
|
||||||
|
0 1 4 4 6 5 32 6 16 0
|
||||||
|
37 5 4 5 5 2 51 6 16 0
|
||||||
|
23 0 5 2 4 6 62 7 16 1
|
||||||
|
0 4 5 1 6 5 48 4 16 1
|
||||||
|
0 7 7 1 6 6 39 3 16 1
|
||||||
|
0 5 3 2 6 5 26 6 16 1
|
||||||
|
40 0 4 2 4 1 38 3 16 1
|
||||||
|
0 5 3 3 4 0 50 4 16 0
|
||||||
|
9 2 5 5 6 4 33 3 16 1
|
||||||
|
15 5 4 3 6 2 36 3 16 0
|
||||||
|
640 5 4 4 6 0 24 6 16 0
|
||||||
|
0 4 3 2 6 0 25 5 16 0
|
||||||
|
0 7 6 5 3 0 62 3 16 0
|
||||||
|
0 2 4 3 3 6 33 3 16 1
|
||||||
|
0 7 6 2 5 6 53 6 16 1
|
||||||
|
22 7 6 2 5 6 68 6 16 1
|
||||||
|
22 7 5 4 6 1 68 3 16 0
|
||||||
|
10 1 6 1 5 5 38 3 16 1
|
||||||
|
29 1 4 5 3 0 58 1 16 0
|
||||||
|
170 7 4 2 6 5 34 6 16 1
|
||||||
|
4 2 4 3 4 1 58 2 16 0
|
||||||
|
11 0 3 4 7 1 35 5 16 0
|
||||||
|
31 3 7 2 6 6 42 6 16 1
|
||||||
|
0 7 4 4 6 0 54 3 16 0
|
||||||
|
0 7 6 2 5 6 69 3 16 1
|
||||||
|
360 2 4 6 5 0 35 4 16 0
|
||||||
|
0 7 6 1 5 6 66 4 16 1
|
||||||
|
900 2 3 2 7 0 58 3 16 0
|
||||||
|
51 5 2 4 7 1 41 3 16 0
|
||||||
|
0 2 5 2 6 4 35 6 16 0
|
||||||
|
110 0 3 3 6 1 40 7 16 0
|
||||||
|
1 7 4 7 2 0 53 1 16 0
|
||||||
|
8 5 6 2 6 5 67 6 16 1
|
||||||
|
5 5 4 4 6 0 32 5 16 0
|
||||||
|
87 4 3 4 6 0 41 4 16 0
|
||||||
|
3 1 2 1 6 1 43 7 16 0
|
||||||
|
51 1 5 3 2 0 65 2 16 0
|
||||||
|
350 7 3 3 7 0 60 5 16 0
|
||||||
|
3 7 5 3 6 0 77 6 16 0
|
||||||
|
630 0 6 5 4 1 35 4 16 0
|
||||||
|
180 4 6 7 5 1 48 4 16 0
|
||||||
|
0 0 6 2 6 6 52 3 16 1
|
||||||
|
35 7 5 3 6 2 43 7 16 0
|
||||||
|
0 7 6 2 6 6 43 5 16 1
|
||||||
|
0 7 6 2 6 6 67 4 16 1
|
||||||
|
6 1 4 4 7 0 56 3 16 0
|
||||||
|
7300 2 3 3 4 0 62 4 16 0
|
||||||
|
2 1 7 7 5 0 62 3 16 0
|
||||||
|
35 3 3 2 6 1 22 6 16 0
|
||||||
|
0 0 2 2 6 1 21 5 16 0
|
||||||
|
45 3 6 1 6 6 34 3 16 1
|
||||||
|
0 7 4 3 5 0 70 3 16 0
|
||||||
|
5 1 3 2 6 2 50 3 16 0
|
||||||
|
35 5 6 1 6 6 42 4 16 1
|
||||||
|
900 5 6 2 6 6 73 3 16 1
|
||||||
|
35 2 3 1 6 0 57 7 16 0
|
||||||
|
0 7 6 1 5 6 40 6 16 1
|
||||||
|
0 2 6 1 6 6 58 6 16 1
|
||||||
|
11 7 4 6 2 1 62 3 16 0
|
||||||
|
40 1 3 5 3 1 44 3 17 0
|
||||||
|
0 3 5 3 7 6 30 6 17 0
|
||||||
|
23 7 3 3 7 0 76 5 17 0
|
||||||
|
270 3 4 3 3 1 50 4 17 0
|
||||||
|
9 1 6 2 5 5 41 4 17 1
|
||||||
|
0 6 6 3 6 6 77 3 17 1
|
||||||
|
0 3 6 3 5 6 35 6 17 1
|
||||||
|
0 7 4 3 4 3 39 4 17 0
|
||||||
|
2 0 4 2 4 4 72 3 17 1
|
||||||
|
0 2 6 2 5 6 42 7 17 1
|
||||||
|
16 7 5 2 6 5 85 2 17 1
|
||||||
|
7300 0 4 2 6 3 79 4 17 0
|
||||||
|
0 5 2 3 6 0 39 4 17 0
|
||||||
|
23 4 6 1 5 6 58 6 17 1
|
||||||
|
42 0 4 2 6 5 27 6 17 1
|
||||||
|
2 4 4 3 5 1 43 5 17 0
|
||||||
|
0 0 3 3 6 0 58 4 17 0
|
||||||
|
42 7 4 3 6 4 28 7 17 1
|
||||||
|
470 2 5 3 6 5 27 6 17 0
|
||||||
|
42 0 3 6 5 2 40 3 17 0
|
||||||
|
0 5 1 3 6 0 43 6 17 0
|
||||||
|
40 7 5 4 6 5 64 3 17 1
|
||||||
|
180 7 2 2 6 0 39 6 17 0
|
||||||
|
110 7 4 2 6 4 76 4 17 1
|
||||||
|
140 5 3 3 6 0 64 5 17 0
|
||||||
|
0 4 4 6 3 2 28 4 17 0
|
||||||
|
0 2 4 2 6 1 45 3 17 0
|
||||||
|
190 1 6 2 6 6 22 3 17 1
|
||||||
|
35 1 3 3 6 1 27 6 17 0
|
||||||
|
45 0 1 2 7 0 31 4 17 0
|
||||||
|
170 1 2 2 7 0 34 6 17 0
|
||||||
|
0 4 2 3 7 0 30 4 17 0
|
||||||
|
0 2 6 1 6 6 64 3 17 1
|
||||||
|
35 0 6 1 6 4 36 6 17 1
|
||||||
|
5 7 4 3 5 4 31 2 17 1
|
||||||
|
350 1 4 3 4 5 37 3 17 1
|
||||||
|
4 0 6 4 2 1 48 3 17 0
|
||||||
|
70 0 5 7 7 3 41 3 17 0
|
||||||
|
8 2 5 2 6 5 25 4 17 1
|
||||||
|
12 5 4 2 6 6 82 3 17 1
|
||||||
|
5 1 6 2 5 4 36 7 17 1
|
||||||
|
0 7 6 2 5 1 47 3 17 1
|
||||||
|
16 6 2 3 6 0 67 4 17 0
|
||||||
|
0 0 4 6 2 5 24 4 17 1
|
||||||
|
9 0 4 5 3 5 33 6 17 1
|
||||||
|
14 7 4 1 5 1 59 4 17 1
|
||||||
|
22 7 7 1 6 6 71 4 17 1
|
||||||
|
0 7 6 2 5 6 36 3 17 1
|
||||||
|
1 3 3 3 5 0 41 3 17 0
|
||||||
|
1600 5 6 2 6 6 38 6 17 1
|
||||||
|
7300 1 6 3 6 6 32 3 17 1
|
||||||
|
19 0 5 6 7 1 32 4 17 0
|
||||||
|
9 6 5 4 6 0 75 6 17 0
|
||||||
|
0 7 5 2 4 4 52 3 17 1
|
||||||
|
1600 5 2 3 6 0 29 7 17 0
|
||||||
|
12 7 3 4 6 0 71 3 17 0
|
||||||
|
1 0 6 2 5 2 33 6 17 0
|
||||||
|
0 3 1 2 7 2 67 7 17 0
|
||||||
|
0 2 6 1 6 6 49 4 17 1
|
||||||
|
0 0 2 3 5 1 31 7 17 0
|
||||||
|
9 7 3 4 6 1 53 2 17 0
|
||||||
|
0 3 2 3 7 0 35 7 17 0
|
||||||
|
170 7 6 2 6 6 49 7 18 1
|
||||||
|
3 7 6 4 4 0 74 3 18 0
|
||||||
|
14 0 6 1 5 6 29 4 18 1
|
||||||
|
1 5 2 3 6 0 27 6 18 0
|
||||||
|
23 5 4 4 6 5 70 4 18 0
|
||||||
|
0 0 6 1 6 6 43 6 18 1
|
||||||
|
1 5 2 3 6 2 47 7 18 0
|
||||||
|
0 4 4 4 6 0 42 7 18 0
|
||||||
|
9 0 6 2 5 6 41 6 18 1
|
||||||
|
130 2 6 4 6 6 37 4 18 1
|
||||||
|
5 5 5 5 4 1 61 3 18 0
|
||||||
|
4 7 4 2 6 0 84 4 18 0
|
||||||
|
2800 7 5 3 7 0 46 6 18 0
|
||||||
|
4 0 4 3 6 5 39 7 18 0
|
||||||
|
6 7 2 3 6 0 53 3 18 0
|
||||||
|
4 7 5 1 6 6 87 6 18 1
|
||||||
|
84 2 4 6 2 0 51 3 18 0
|
||||||
|
0 0 5 2 5 4 37 5 18 1
|
||||||
|
9 1 4 3 5 4 37 6 18 1
|
||||||
|
290 0 5 2 6 6 40 6 18 1
|
||||||
|
0 3 4 4 6 2 46 6 18 0
|
||||||
|
9 7 1 4 1 3 44 3 18 1
|
||||||
|
0 2 4 3 6 1 37 3 18 0
|
||||||
|
0 7 4 2 6 2 55 7 18 0
|
||||||
|
22 0 5 2 5 1 40 6 18 1
|
||||||
|
0 5 3 2 6 1 42 7 18 0
|
||||||
|
640 7 6 2 6 6 40 3 18 1
|
||||||
|
10 0 6 2 6 6 32 3 18 1
|
||||||
|
4 5 5 2 5 4 58 7 18 1
|
||||||
|
0 2 5 1 6 4 37 3 18 1
|
||||||
|
640 4 4 5 5 5 51 3 18 1
|
||||||
|
110 5 3 5 4 5 44 5 18 0
|
||||||
|
0 2 4 5 2 0 23 2 18 0
|
||||||
|
0 2 6 2 6 6 38 3 18 1
|
||||||
|
67 0 3 4 6 0 42 7 18 0
|
||||||
|
520 7 4 2 5 4 60 4 18 1
|
||||||
|
0 3 4 3 6 1 34 4 18 0
|
||||||
|
51 1 6 2 6 6 49 3 18 1
|
||||||
|
0 0 4 2 3 4 40 3 18 1
|
||||||
|
2800 7 2 2 7 0 23 4 18 0
|
||||||
|
110 2 2 3 6 0 47 3 18 0
|
||||||
|
7300 7 4 4 7 0 44 5 18 0
|
||||||
|
7300 5 5 2 6 1 37 7 18 1
|
||||||
|
0 2 6 1 6 5 26 5 18 1
|
||||||
|
16 7 7 6 5 2 51 4 18 0
|
||||||
|
140 4 4 4 6 0 69 3 18 0
|
||||||
|
54 2 2 2 7 0 36 6 18 0
|
||||||
|
2 2 2 3 6 2 28 4 18 0
|
||||||
|
470 4 5 2 6 5 40 4 19 1
|
||||||
|
0 1 6 1 6 5 45 4 19 1
|
||||||
|
75 3 5 2 5 5 75 6 19 1
|
||||||
|
11 2 6 2 5 5 32 6 19 1
|
||||||
|
0 0 3 2 6 1 27 3 19 0
|
||||||
|
2800 6 5 2 5 3 63 6 19 1
|
||||||
|
140 1 5 3 6 6 79 7 19 1
|
||||||
|
9 7 3 2 6 1 40 6 19 0
|
||||||
|
7300 2 4 3 6 1 57 6 19 0
|
||||||
|
45 3 3 4 5 1 44 6 19 0
|
||||||
|
1600 7 5 2 5 6 78 6 19 1
|
||||||
|
9 7 4 5 5 5 58 7 19 0
|
||||||
|
0 7 2 2 7 1 55 3 19 0
|
||||||
|
130 4 3 4 6 2 25 6 19 0
|
||||||
|
9 0 2 3 6 0 27 7 19 0
|
||||||
|
3500 3 4 2 7 2 21 3 19 0
|
||||||
|
0 3 6 2 6 5 37 4 19 1
|
||||||
|
0 0 7 3 4 5 31 3 19 1
|
||||||
|
35 7 2 4 6 0 85 7 19 0
|
||||||
|
900 1 4 2 6 5 33 3 19 0
|
||||||
|
27 7 3 7 4 3 63 2 19 1
|
||||||
|
900 7 4 4 6 1 51 3 19 0
|
||||||
|
0 2 3 5 2 0 46 3 19 0
|
||||||
|
64 2 5 2 6 6 26 5 19 1
|
||||||
|
4 7 3 3 6 0 57 6 19 0
|
||||||
|
0 3 4 2 6 1 48 3 19 1
|
||||||
|
0 2 7 1 5 5 55 3 19 1
|
||||||
|
53 0 3 6 4 1 37 5 19 0
|
||||||
|
290 2 6 2 6 6 39 3 19 1
|
||||||
|
15 2 2 3 4 1 24 3 19 1
|
||||||
|
0 0 6 3 6 6 30 6 19 1
|
||||||
|
35 0 3 4 6 1 36 7 19 0
|
||||||
|
0 0 5 4 6 4 45 3 19 0
|
||||||
|
470 1 5 2 6 2 29 3 19 0
|
||||||
|
16 7 6 3 6 5 41 4 19 1
|
||||||
|
40 1 5 3 5 4 28 5 19 1
|
||||||
|
290 7 2 2 7 0 35 4 19 0
|
||||||
|
4 7 2 3 6 0 46 4 19 0
|
||||||
|
1 7 1 2 7 1 55 4 19 0
|
||||||
|
140 3 2 3 6 6 65 4 19 0
|
||||||
|
110 1 4 2 6 1 22 4 19 0
|
||||||
|
0 1 4 4 6 1 41 4 19 0
|
||||||
|
900 1 2 3 6 2 43 7 19 0
|
||||||
|
0 2 4 3 6 0 49 4 19 0
|
||||||
|
0 4 4 2 5 4 35 7 19 1
|
||||||
|
51 7 3 3 5 1 80 3 19 0
|
||||||
|
71 2 4 6 2 0 57 4 19 0
|
||||||
|
900 4 3 3 6 0 48 3 19 0
|
||||||
|
83 4 1 4 6 2 43 6 19 0
|
||||||
|
360 3 4 4 7 0 34 4 19 0
|
||||||
|
180 7 2 2 4 0 44 5 19 0
|
||||||
|
1 3 6 1 6 4 49 6 20 1
|
||||||
|
22 7 5 2 7 2 43 7 20 0
|
||||||
|
1 0 4 3 5 6 39 4 20 1
|
||||||
|
2 2 4 3 7 1 27 3 20 0
|
||||||
|
34 4 6 2 6 6 30 6 20 1
|
||||||
|
33 2 6 2 6 4 33 6 20 1
|
||||||
|
10 1 2 3 7 0 32 5 20 0
|
||||||
|
290 7 5 2 6 6 31 6 20 1
|
||||||
|
0 2 5 3 7 2 27 5 20 0
|
||||||
|
76 3 2 2 6 0 36 4 20 0
|
||||||
|
20 7 3 2 6 2 45 5 20 0
|
||||||
|
0 5 4 6 5 2 51 3 20 0
|
||||||
|
0 0 6 2 3 5 36 4 20 1
|
||||||
|
0 7 6 4 6 4 47 7 20 1
|
||||||
|
470 4 4 4 6 1 42 6 20 0
|
||||||
|
9 6 6 1 5 6 70 6 20 1
|
||||||
|
0 0 3 3 6 3 40 4 20 0
|
||||||
|
0 7 2 5 7 0 38 3 20 0
|
||||||
|
8 0 5 1 6 4 29 3 20 1
|
||||||
|
1 0 4 2 5 6 58 3 20 1
|
||||||
|
1 7 4 6 1 2 28 3 20 0
|
||||||
|
7 1 6 3 6 5 30 4 20 1
|
||||||
|
0 3 4 3 6 2 48 5 20 0
|
||||||
|
170 0 6 2 6 6 35 4 20 1
|
||||||
|
270 3 6 7 6 0 38 3 20 0
|
||||||
|
0 0 4 2 5 4 56 7 20 1
|
||||||
|
13 4 5 3 6 5 62 4 20 1
|
||||||
|
10 7 6 1 6 5 56 4 20 1
|
||||||
|
22 0 6 2 5 6 32 3 20 1
|
||||||
|
640 5 4 5 7 1 50 5 20 0
|
||||||
|
900 3 3 4 6 1 45 6 20 0
|
||||||
|
22 7 3 3 4 0 36 6 20 0
|
||||||
|
62 1 6 2 6 6 49 4 20 1
|
||||||
|
110 2 7 1 6 6 31 6 20 1
|
||||||
|
84 4 2 3 6 2 33 7 20 0
|
||||||
|
0 1 6 2 6 6 35 4 20 1
|
||||||
|
13 2 4 2 5 4 73 4 20 1
|
||||||
|
20 7 6 1 5 6 54 5 20 1
|
||||||
|
0 3 4 2 7 1 48 3 20 0
|
||||||
|
16 3 6 2 6 4 65 4 20 1
|
||||||
|
6 7 5 6 5 6 46 4 20 0
|
||||||
|
12 0 2 2 6 0 45 7 20 0
|
||||||
|
0 7 6 2 5 6 65 7 20 1
|
||||||
|
0 0 6 2 6 6 45 6 20 1
|
||||||
|
170 7 4 1 5 4 89 7 20 1
|
||||||
|
100 0 5 3 6 4 32 4 20 0
|
||||||
|
5 4 6 4 4 1 38 4 20 0
|
||||||
|
3500 5 2 4 2 5 33 4 20 1
|
||||||
|
71 4 6 2 5 6 49 7 20 1
|
||||||
|
4 7 5 2 6 5 64 6 20 1
|
||||||
|
7300 5 5 4 7 1 55 4 20 0
|
||||||
|
7 0 3 3 5 1 56 7 20 0
|
||||||
|
290 4 4 5 3 5 30 6 20 0
|
||||||
|
0 0 5 4 6 5 41 7 20 1
|
||||||
|
0 1 6 2 5 6 39 5 20 1
|
||||||
|
520 7 4 4 6 4 34 3 20 0
|
||||||
|
430 5 4 5 7 1 53 3 20 0
|
||||||
|
9 2 3 3 6 2 40 6 20 0
|
||||||
|
40 0 3 3 6 1 48 6 20 0
|
||||||
|
2 7 6 5 5 5 59 4 20 0
|
||||||
|
75 7 6 2 4 4 54 4 20 1
|
||||||
|
170 0 3 5 6 1 41 6 20 0
|
||||||
|
170 1 2 3 6 0 41 6 20 0
|
||||||
|
640 5 5 3 6 4 63 7 20 0
|
||||||
|
2800 1 7 1 6 4 39 3 20 1
|
||||||
|
9 3 7 2 5 4 46 3 20 0
|
||||||
|
150 7 4 3 6 0 55 3 20 0
|
||||||
|
0 7 4 1 5 3 42 3 20 1
|
||||||
|
0 7 6 3 6 4 58 2 20 1
|
||||||
|
100 0 3 2 6 2 42 4 20 0
|
||||||
|
33 7 4 3 4 2 40 6 20 1
|
||||||
|
310 7 5 2 5 1 56 6 20 1
|
||||||
|
53 1 4 5 2 2 37 4 20 0
|
||||||
|
13 2 3 2 6 2 37 6 20 0
|
||||||
|
290 6 5 1 5 5 49 6 20 1
|
||||||
|
310 7 6 1 6 6 63 4 20 1
|
||||||
|
0 7 6 2 5 6 30 7 20 1
|
||||||
|
54 7 4 2 6 4 62 5 20 1
|
||||||
|
1600 0 4 3 6 1 30 6 20 0
|
||||||
|
14 0 6 3 6 6 34 6 20 1
|
||||||
|
25 7 6 3 6 5 41 4 20 1
|
||||||
|
45 0 4 5 4 6 43 3 20 1
|
||||||
|
20 2 5 2 6 4 33 5 20 0
|
||||||
|
18 6 4 3 5 5 67 7 20 1
|
||||||
|
740 7 5 1 6 0 55 5 20 1
|
||||||
|
9 6 2 2 6 0 33 5 20 0
|
||||||
|
5 0 6 1 5 5 61 4 20 1
|
||||||
|
7300 7 4 1 6 4 45 3 20 0
|
||||||
|
81 7 4 3 7 0 34 3 20 0
|
||||||
|
190 1 6 2 6 6 35 3 20 1
|
||||||
|
51 1 4 4 5 2 50 6 20 0
|
||||||
|
7300 7 3 4 2 0 38 4 20 0
|
||||||
|
350 0 6 2 6 5 56 3 20 1
|
||||||
|
27 2 4 1 6 5 31 3 20 1
|
||||||
|
33 5 6 1 4 5 40 7 20 1
|
||||||
|
50 4 3 2 6 0 44 3 20 0
|
||||||
|
1 7 6 5 2 2 39 4 20 0
|
||||||
|
11 1 4 3 6 1 45 3 20 0
|
||||||
|
51 5 3 3 6 0 72 6 20 0
|
||||||
|
160 7 2 3 5 1 44 7 20 0
|
||||||
|
16 7 1 3 6 0 61 7 21 0
|
||||||
|
110 6 6 2 6 6 34 7 21 1
|
||||||
|
110 4 5 2 5 5 61 6 21 1
|
||||||
|
13 6 3 3 7 1 67 4 21 0
|
||||||
|
220 7 4 1 4 6 38 6 21 1
|
||||||
|
470 7 4 2 6 1 50 6 21 0
|
||||||
|
22 6 6 1 6 6 62 4 21 1
|
||||||
|
9 2 6 3 6 5 36 6 21 1
|
||||||
|
22 3 6 3 5 4 50 6 21 1
|
||||||
|
190 2 4 2 7 1 30 6 21 0
|
||||||
|
100 5 6 2 6 6 59 5 21 1
|
||||||
|
14 7 3 3 6 0 62 6 21 0
|
||||||
|
0 7 4 3 6 3 40 4 21 0
|
||||||
|
180 1 6 1 6 6 30 4 21 1
|
||||||
|
3 6 3 2 6 2 47 3 21 0
|
||||||
|
51 2 5 2 6 1 41 6 21 0
|
||||||
|
9 2 6 1 6 6 35 7 21 1
|
||||||
|
0 7 5 2 6 6 45 4 21 1
|
||||||
|
0 2 4 2 6 4 34 6 21 1
|
||||||
|
71 2 5 7 2 2 55 3 21 0
|
||||||
|
290 1 5 3 6 2 37 4 21 0
|
||||||
|
45 3 5 2 4 4 61 7 21 1
|
||||||
|
0 3 4 5 4 1 62 2 21 0
|
||||||
|
26 1 5 2 6 6 54 6 21 1
|
||||||
|
87 3 2 3 6 1 33 6 21 0
|
||||||
|
0 0 2 3 6 1 50 6 21 0
|
||||||
|
630 0 3 4 7 2 37 3 21 0
|
||||||
|
50 5 5 3 6 3 44 3 21 1
|
||||||
|
35 7 4 3 4 3 78 3 21 0
|
||||||
|
180 7 6 2 4 6 56 3 21 0
|
||||||
|
32 0 2 3 4 2 29 6 21 0
|
||||||
|
0 7 3 2 5 4 52 6 21 0
|
||||||
|
51 1 6 4 5 1 31 3 21 0
|
||||||
|
40 0 6 3 6 6 34 7 21 1
|
||||||
|
0 2 5 3 6 5 31 7 21 1
|
||||||
|
0 7 4 3 5 2 43 6 21 0
|
||||||
|
0 1 4 4 3 5 31 3 21 1
|
||||||
|
1 6 6 1 6 6 63 7 21 1
|
||||||
|
7 2 2 3 7 4 38 4 21 0
|
||||||
|
0 2 6 2 6 6 31 5 21 1
|
||||||
|
71 2 2 1 7 0 64 3 21 0
|
||||||
|
75 2 3 2 5 5 55 7 21 0
|
||||||
|
55 1 2 2 6 0 41 3 21 0
|
||||||
|
290 4 3 4 5 5 38 4 21 1
|
||||||
|
88 4 6 3 6 5 28 6 21 1
|
||||||
|
0 7 4 3 5 5 42 5 21 1
|
||||||
|
16 7 2 4 3 1 43 4 21 0
|
||||||
|
75 1 5 1 6 5 37 4 21 1
|
||||||
|
220 1 4 2 6 3 47 5 21 0
|
||||||
|
3 5 5 1 6 5 52 7 21 1
|
||||||
|
130 0 5 2 6 5 32 4 21 1
|
||||||
|
0 5 4 4 3 2 29 3 21 0
|
||||||
|
110 2 6 2 5 4 56 3 21 1
|
||||||
|
12 7 4 3 5 5 63 7 21 1
|
||||||
|
180 3 3 3 6 1 35 5 21 0
|
||||||
|
93 7 4 3 5 3 36 4 21 1
|
||||||
|
170 7 4 2 7 0 75 5 21 0
|
||||||
|
31 5 3 3 6 1 48 6 21 0
|
||||||
|
62 4 7 2 6 6 36 5 21 1
|
||||||
|
30 4 4 3 6 2 34 6 21 0
|
||||||
|
66 7 4 3 7 1 35 5 21 0
|
||||||
|
3 3 5 2 5 6 50 4 21 1
|
||||||
|
18 3 5 2 5 6 39 7 21 1
|
||||||
|
350 5 5 4 6 5 70 7 21 1
|
||||||
|
71 7 4 2 5 6 76 3 21 1
|
||||||
|
3500 5 5 2 5 5 35 6 21 1
|
||||||
|
0 0 3 4 3 3 53 7 21 1
|
||||||
|
360 6 5 2 6 4 46 6 21 1
|
||||||
|
81 2 5 2 5 4 34 4 21 1
|
||||||
|
350 5 4 4 6 5 69 4 21 0
|
||||||
|
190 1 5 2 5 1 32 3 21 0
|
||||||
|
0 7 5 1 6 6 50 2 21 1
|
||||||
|
290 1 5 3 6 6 35 6 21 1
|
||||||
|
18 0 6 1 6 6 67 5 21 1
|
||||||
|
11 3 5 5 6 5 47 3 21 0
|
||||||
|
2 2 6 3 5 4 50 6 21 1
|
||||||
|
570 0 6 2 6 4 32 3 21 1
|
||||||
|
310 3 5 4 6 2 58 5 21 0
|
||||||
|
1 7 3 2 7 0 49 7 21 0
|
||||||
|
0 2 6 3 6 1 43 4 21 1
|
||||||
|
35 1 5 2 6 5 24 6 21 1
|
||||||
|
22 7 5 3 4 2 58 7 21 0
|
||||||
|
2 1 2 2 5 0 43 4 21 0
|
||||||
|
0 7 4 3 6 5 59 3 21 1
|
||||||
|
0 3 6 1 6 6 40 5 21 1
|
||||||
|
310 0 5 2 7 0 35 4 21 0
|
||||||
|
470 5 2 3 5 1 48 4 21 0
|
||||||
|
0 4 6 1 6 6 40 3 21 1
|
||||||
|
270 3 3 2 7 0 48 3 21 0
|
||||||
|
110 0 2 4 6 0 47 7 21 0
|
||||||
|
50 3 6 4 1 1 23 3 21 0
|
||||||
|
0 0 6 1 6 6 38 3 21 1
|
||||||
|
3 7 6 2 6 6 81 7 21 1
|
||||||
|
31 5 3 3 6 1 48 6 21 0
|
||||||
|
22 7 5 6 2 4 52 3 21 0
|
||||||
|
83 2 4 1 6 3 24 6 21 1
|
||||||
|
9 0 4 2 6 1 21 5 21 0
|
||||||
|
5 7 5 2 6 5 70 6 21 1
|
||||||
|
0 7 7 1 7 6 24 6 21 1
|
||||||
|
0 7 4 3 5 1 57 7 21 0
|
||||||
|
0 6 3 5 6 4 37 6 21 0
|
||||||
|
27 1 3 5 3 5 25 5 21 0
|
||||||
|
110 1 6 5 1 1 33 3 21 0
|
||||||
|
0 7 4 6 3 0 45 3 22 0
|
||||||
|
0 3 5 2 7 5 42 6 22 0
|
||||||
|
350 3 3 3 6 2 47 6 22 0
|
||||||
|
0 7 6 1 6 6 51 7 22 1
|
||||||
|
5 7 5 4 6 5 85 2 22 0
|
||||||
|
15 4 3 3 6 2 32 7 22 0
|
||||||
|
35 7 3 3 6 2 31 7 22 0
|
||||||
|
0 2 4 3 6 2 23 6 22 0
|
||||||
|
75 3 3 3 6 0 42 6 22 0
|
||||||
|
0 5 6 2 5 5 55 6 22 1
|
||||||
|
16 7 6 2 6 6 45 6 22 1
|
||||||
|
0 1 6 2 5 6 35 7 22 1
|
||||||
|
0 0 2 4 6 0 45 6 22 0
|
||||||
|
0 0 3 3 5 2 42 3 22 0
|
||||||
|
4 1 6 2 6 5 37 4 22 1
|
||||||
|
62 0 4 4 4 5 38 3 22 1
|
||||||
|
0 3 2 2 6 1 47 7 22 0
|
||||||
|
4 7 4 2 6 2 32 6 22 0
|
||||||
|
56 2 5 2 6 5 35 6 22 1
|
||||||
|
2 6 4 2 6 5 38 4 22 1
|
||||||
|
0 0 4 4 4 3 40 3 22 0
|
||||||
|
75 7 6 2 5 6 62 6 22 1
|
||||||
|
10 2 2 2 6 1 28 7 22 0
|
||||||
|
0 6 6 2 5 6 59 5 22 1
|
||||||
|
0 2 1 2 6 0 25 6 22 0
|
||||||
|
220 2 4 2 6 6 31 6 22 1
|
||||||
|
0 1 7 2 6 6 45 4 22 1
|
||||||
|
75 0 5 2 7 5 42 6 22 0
|
||||||
|
0 3 2 2 5 1 56 7 22 0
|
||||||
|
140 5 3 3 6 1 47 7 22 0
|
||||||
|
290 1 6 2 5 5 38 6 22 1
|
||||||
|
350 7 4 3 7 2 47 6 22 0
|
||||||
|
55 1 5 2 6 6 49 4 22 1
|
||||||
|
31 3 3 3 6 2 29 7 22 0
|
||||||
|
17 7 2 2 6 0 57 6 22 0
|
||||||
|
51 4 6 3 5 6 68 3 22 1
|
||||||
|
140 7 2 2 4 0 76 6 22 0
|
||||||
|
9 4 6 2 6 5 66 6 22 1
|
||||||
|
0 7 5 1 6 4 59 4 22 1
|
||||||
|
640 7 5 3 6 4 37 7 22 0
|
||||||
|
32 2 5 2 6 6 38 6 22 1
|
||||||
|
5 7 6 2 6 5 47 7 22 1
|
||||||
|
8 1 5 2 4 5 36 7 22 1
|
||||||
|
18 0 5 4 6 0 45 7 22 0
|
||||||
|
0 6 5 2 6 5 39 7 22 1
|
||||||
|
0 1 3 2 6 1 34 6 22 0
|
||||||
|
0 3 6 6 4 0 49 4 22 0
|
||||||
|
31 2 6 2 6 5 36 6 22 1
|
||||||
|
350 7 6 1 6 6 81 5 22 1
|
||||||
|
20 1 5 2 6 4 29 4 22 1
|
||||||
|
70 3 5 3 4 0 45 6 22 0
|
||||||
|
31 3 5 2 6 5 21 4 22 1
|
||||||
|
3 7 2 4 3 6 33 6 22 1
|
||||||
|
9 7 4 4 2 2 44 3 23 0
|
||||||
|
59 1 2 2 6 4 52 7 23 0
|
||||||
|
27 2 3 5 7 1 38 4 23 0
|
||||||
|
51 4 2 3 6 2 44 5 23 0
|
||||||
|
9 7 6 2 6 6 87 7 23 1
|
||||||
|
0 2 7 2 6 5 22 3 23 1
|
||||||
|
88 0 3 3 5 2 32 6 23 1
|
||||||
|
67 0 4 5 6 4 69 3 23 0
|
||||||
|
29 2 6 2 5 6 49 6 23 1
|
||||||
|
5 0 6 2 6 6 53 3 23 1
|
||||||
|
0 0 6 1 6 5 44 6 23 1
|
||||||
|
900 1 6 2 7 5 34 6 23 1
|
||||||
|
18 1 6 1 5 5 55 7 23 1
|
||||||
|
190 2 3 3 6 0 35 6 23 0
|
||||||
|
2 7 4 5 5 3 55 3 23 0
|
||||||
|
5 3 6 3 5 5 27 6 23 1
|
||||||
|
56 3 4 4 5 1 26 6 23 0
|
||||||
|
75 7 4 2 6 5 54 6 23 0
|
||||||
|
56 0 5 2 6 5 42 4 23 1
|
||||||
|
0 5 6 1 6 6 57 7 23 1
|
||||||
|
0 0 7 1 4 6 54 6 23 1
|
||||||
|
75 6 6 3 6 6 55 6 23 1
|
||||||
|
1600 7 5 3 6 6 50 7 23 1
|
||||||
|
15 0 5 5 6 1 57 7 23 0
|
||||||
|
19 3 5 3 7 3 46 4 23 0
|
||||||
|
16 7 6 2 6 6 53 6 23 1
|
||||||
|
42 2 3 3 5 0 32 7 23 0
|
||||||
|
18 5 5 2 5 3 53 5 23 1
|
||||||
|
0 3 3 3 6 1 39 6 23 0
|
||||||
|
310 1 5 2 5 4 47 6 23 1
|
||||||
|
1600 7 5 2 4 2 57 4 23 0
|
||||||
|
23 5 6 3 6 6 49 6 23 1
|
||||||
|
20 1 5 4 6 5 31 5 23 0
|
||||||
|
51 5 5 3 5 5 43 6 23 1
|
||||||
|
0 2 5 2 5 4 44 6 23 1
|
||||||
|
0 4 4 3 6 0 39 6 23 0
|
||||||
|
0 2 6 1 6 5 49 4 23 1
|
||||||
|
18 7 5 4 6 4 72 6 23 1
|
||||||
|
7300 7 5 2 6 5 50 6 23 1
|
||||||
|
110 1 5 2 6 6 28 4 23 1
|
||||||
|
0 0 5 2 7 3 48 7 23 1
|
||||||
|
3500 1 3 4 7 1 32 6 23 0
|
||||||
|
720 7 5 5 5 1 63 4 23 0
|
||||||
|
9 4 4 5 6 5 36 4 23 1
|
||||||
|
47 7 6 3 6 6 36 6 23 1
|
||||||
|
350 7 3 2 7 2 53 3 23 0
|
||||||
|
0 5 2 2 6 2 44 7 23 0
|
||||||
|
0 0 4 2 6 6 41 7 24 1
|
||||||
|
83 0 2 3 6 1 56 7 24 0
|
||||||
|
1 4 4 4 6 2 63 7 24 0
|
||||||
|
190 7 2 4 6 0 52 6 24 0
|
||||||
|
0 7 3 3 7 2 43 7 24 0
|
||||||
|
12 7 4 3 6 2 40 3 24 0
|
||||||
|
9 5 5 1 7 4 69 4 24 1
|
||||||
|
23 7 2 2 6 0 49 7 24 0
|
||||||
|
9 1 3 3 6 2 65 7 24 0
|
||||||
|
18 5 6 1 6 5 53 7 24 1
|
||||||
|
0 5 5 3 5 6 50 4 24 1
|
||||||
|
12 3 2 4 6 0 27 5 24 0
|
||||||
|
0 6 5 3 5 5 44 6 24 1
|
||||||
|
170 2 2 3 5 0 54 7 24 0
|
||||||
|
0 7 4 2 6 4 33 5 24 1
|
||||||
|
9 0 4 4 6 3 48 7 24 0
|
||||||
|
23 3 5 2 6 5 54 5 24 1
|
||||||
|
0 0 6 2 5 6 56 3 24 1
|
||||||
|
9 1 2 4 7 2 34 7 24 0
|
||||||
|
290 7 6 4 7 6 41 6 24 0
|
||||||
|
1 0 5 1 5 6 40 6 24 1
|
||||||
|
350 1 7 2 6 6 55 6 24 1
|
||||||
|
20 0 4 3 5 5 38 6 24 1
|
||||||
|
0 3 6 2 6 6 40 6 24 1
|
||||||
|
23 1 6 1 6 6 46 4 24 1
|
||||||
|
150 4 3 3 4 0 26 6 24 0
|
||||||
|
31 0 5 2 7 3 49 6 24 0
|
||||||
|
0 7 4 1 5 6 51 5 24 1
|
||||||
|
9 2 5 2 6 4 46 6 24 0
|
||||||
|
47 0 3 4 6 2 40 7 24 0
|
||||||
|
900 0 3 4 7 2 30 5 24 0
|
||||||
|
83 3 2 3 6 2 45 5 24 0
|
||||||
|
18 7 5 4 6 4 52 7 24 1
|
||||||
|
0 0 6 1 5 6 36 6 24 1
|
||||||
|
20 0 4 3 5 3 49 6 24 0
|
||||||
|
24 7 3 4 5 1 38 7 24 0
|
||||||
|
18 0 2 4 6 1 51 7 24 0
|
||||||
|
9 3 3 2 5 1 47 6 24 0
|
||||||
|
0 1 6 1 5 6 52 7 24 1
|
||||||
|
9 0 6 2 6 6 33 6 24 1
|
||||||
|
0 4 4 2 6 6 50 4 24 1
|
||||||
|
18 7 6 2 5 4 48 7 24 1
|
||||||
|
19 3 2 2 6 0 36 6 24 0
|
||||||
|
31 3 2 3 6 1 35 7 24 0
|
||||||
|
3500 7 7 3 5 4 34 7 24 0
|
||||||
|
0 7 2 4 5 2 53 6 24 0
|
||||||
|
33 0 4 3 6 2 33 7 24 0
|
||||||
|
0 1 6 3 6 6 52 6 24 1
|
||||||
|
18 3 4 3 6 4 44 7 24 0
|
||||||
|
0 0 3 4 4 0 48 6 24 0
|
||||||
|
31 3 5 2 6 5 20 4 24 1
|
||||||
|
0 5 3 2 4 6 45 6 24 1
|
||||||
|
59 7 4 2 6 2 70 3 24 0
|
||||||
|
0 0 3 3 4 2 39 3 24 0
|
||||||
|
7300 7 3 3 5 1 40 7 24 1
|
||||||
|
75 4 5 2 7 5 62 6 24 1
|
||||||
|
0 7 5 2 6 4 46 6 24 1
|
||||||
|
27 7 4 4 7 2 46 3 24 0
|
||||||
|
1600 7 4 2 5 6 56 7 24 1
|
||||||
|
0 7 6 3 6 6 55 7 24 1
|
||||||
|
0 7 6 2 6 6 41 4 24 1
|
||||||
|
7300 1 2 3 6 0 43 7 24 0
|
||||||
|
16 7 7 1 7 6 34 3 24 1
|
||||||
|
0 7 7 1 6 4 73 6 24 1
|
||||||
|
0 7 5 2 6 6 50 6 24 1
|
||||||
|
0 3 6 2 7 5 43 6 24 1
|
||||||
|
0 6 6 2 5 6 46 7 24 1
|
||||||
|
18 7 4 2 6 3 61 7 24 1
|
||||||
|
@ -0,0 +1,116 @@
|
|||||||
|
"""American National Election Survey 1996"""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """This is public domain."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
http://www.electionstudies.org/
|
||||||
|
|
||||||
|
The American National Election Studies.
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """This data is a subset of the American National Election Studies of 1996."""
|
||||||
|
|
||||||
|
DESCRLONG = DESCRSHORT
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of observations - 944
|
||||||
|
Numner of variables - 10
|
||||||
|
|
||||||
|
Variables name definitions::
|
||||||
|
|
||||||
|
popul - Census place population in 1000s
|
||||||
|
TVnews - Number of times per week that respondent watches TV news.
|
||||||
|
PID - Party identification of respondent.
|
||||||
|
0 - Strong Democrat
|
||||||
|
1 - Weak Democrat
|
||||||
|
2 - Independent-Democrat
|
||||||
|
3 - Independent-Indpendent
|
||||||
|
4 - Independent-Republican
|
||||||
|
5 - Weak Republican
|
||||||
|
6 - Strong Republican
|
||||||
|
age : Age of respondent.
|
||||||
|
educ - Education level of respondent
|
||||||
|
1 - 1-8 grades
|
||||||
|
2 - Some high school
|
||||||
|
3 - High school graduate
|
||||||
|
4 - Some college
|
||||||
|
5 - College degree
|
||||||
|
6 - Master's degree
|
||||||
|
7 - PhD
|
||||||
|
income - Income of household
|
||||||
|
1 - None or less than $2,999
|
||||||
|
2 - $3,000-$4,999
|
||||||
|
3 - $5,000-$6,999
|
||||||
|
4 - $7,000-$8,999
|
||||||
|
5 - $9,000-$9,999
|
||||||
|
6 - $10,000-$10,999
|
||||||
|
7 - $11,000-$11,999
|
||||||
|
8 - $12,000-$12,999
|
||||||
|
9 - $13,000-$13,999
|
||||||
|
10 - $14,000-$14.999
|
||||||
|
11 - $15,000-$16,999
|
||||||
|
12 - $17,000-$19,999
|
||||||
|
13 - $20,000-$21,999
|
||||||
|
14 - $22,000-$24,999
|
||||||
|
15 - $25,000-$29,999
|
||||||
|
16 - $30,000-$34,999
|
||||||
|
17 - $35,000-$39,999
|
||||||
|
18 - $40,000-$44,999
|
||||||
|
19 - $45,000-$49,999
|
||||||
|
20 - $50,000-$59,999
|
||||||
|
21 - $60,000-$74,999
|
||||||
|
22 - $75,000-89,999
|
||||||
|
23 - $90,000-$104,999
|
||||||
|
24 - $105,000 and over
|
||||||
|
vote - Expected vote
|
||||||
|
0 - Clinton
|
||||||
|
1 - Dole
|
||||||
|
The following 3 variables all take the values:
|
||||||
|
1 - Extremely liberal
|
||||||
|
2 - Liberal
|
||||||
|
3 - Slightly liberal
|
||||||
|
4 - Moderate
|
||||||
|
5 - Slightly conservative
|
||||||
|
6 - Conservative
|
||||||
|
7 - Extremely Conservative
|
||||||
|
selfLR - Respondent's self-reported political leanings from "Left"
|
||||||
|
to "Right".
|
||||||
|
ClinLR - Respondents impression of Bill Clinton's political
|
||||||
|
leanings from "Left" to "Right".
|
||||||
|
DoleLR - Respondents impression of Bob Dole's political leanings
|
||||||
|
from "Left" to "Right".
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""Load the anes96 data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=5, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""Load the anes96 data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=5, dtype=float)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/anes96.csv',"rb"), delimiter="\t",
|
||||||
|
names = True, dtype=float)
|
||||||
|
return data
|
||||||
@ -0,0 +1,945 @@
|
|||||||
|
popul TVnews selfLR ClinLR DoleLR PID age educ income vote reldist
|
||||||
|
0 7 7 1 6 6 36 3 1 1 -5
|
||||||
|
190 1 3 3 5 1 20 4 1 0 2
|
||||||
|
31 7 2 2 6 1 24 6 1 0 4
|
||||||
|
83 4 3 4 5 1 28 6 1 0 1
|
||||||
|
640 7 5 6 4 0 68 6 1 0 0
|
||||||
|
110 3 3 4 6 1 21 4 1 0 2
|
||||||
|
100 7 5 6 4 1 77 4 1 0 0
|
||||||
|
31 1 5 4 5 4 21 4 1 0 -1
|
||||||
|
180 7 4 6 3 3 31 4 1 0 -1
|
||||||
|
2800 0 3 3 7 0 39 3 1 0 4
|
||||||
|
1600 0 3 2 4 4 26 2 1 0 0
|
||||||
|
330 5 4 3 6 1 31 4 1 0 1
|
||||||
|
190 2 5 4 6 5 22 4 1 1 0
|
||||||
|
100 7 4 4 6 0 42 5 1 0 2
|
||||||
|
1000 7 5 7 4 0 74 1 1 0 -1
|
||||||
|
0 7 6 7 5 0 62 3 1 0 0
|
||||||
|
130 7 4 4 5 1 58 3 1 0 1
|
||||||
|
5 5 3 3 6 1 24 6 1 0 3
|
||||||
|
33 7 6 2 6 5 51 4 1 1 -4
|
||||||
|
19 2 2 1 4 0 36 3 2 0 1
|
||||||
|
74 7 4 4 7 2 88 2 2 0 3
|
||||||
|
190 0 2 4 6 2 20 4 2 0 2
|
||||||
|
12 3 4 6 3 2 27 3 2 0 -1
|
||||||
|
0 7 6 1 6 6 44 4 2 1 -5
|
||||||
|
19 0 4 2 2 1 45 3 2 0 0
|
||||||
|
0 2 4 3 6 1 21 4 2 0 1
|
||||||
|
390 5 3 4 7 1 40 5 2 0 3
|
||||||
|
40 7 4 3 4 0 40 6 2 0 -1
|
||||||
|
3 3 5 5 4 1 48 3 2 0 1
|
||||||
|
450 3 4 7 1 0 34 3 2 0 0
|
||||||
|
350 0 3 4 7 2 26 2 2 0 3
|
||||||
|
64 3 4 4 2 1 60 2 3 0 2
|
||||||
|
3 0 4 4 3 0 32 3 3 0 1
|
||||||
|
0 1 4 3 7 1 31 3 3 0 2
|
||||||
|
640 7 7 5 7 4 33 3 3 1 -2
|
||||||
|
0 7 3 4 6 0 57 3 3 0 2
|
||||||
|
12 7 4 3 6 1 84 3 3 0 1
|
||||||
|
62 6 7 2 7 5 75 3 3 1 -5
|
||||||
|
31 2 7 2 6 6 19 4 3 1 -4
|
||||||
|
0 1 3 2 6 1 47 6 3 0 2
|
||||||
|
180 6 5 5 5 0 51 2 3 0 0
|
||||||
|
640 3 6 4 4 5 40 3 3 0 0
|
||||||
|
110 0 2 3 6 1 22 6 3 0 3
|
||||||
|
100 1 7 7 5 6 35 2 3 0 2
|
||||||
|
100 7 4 4 7 2 43 5 3 0 3
|
||||||
|
11 3 6 6 3 2 76 6 3 0 3
|
||||||
|
0 7 4 3 1 6 45 3 3 1 2
|
||||||
|
4 7 4 6 6 0 88 2 3 0 0
|
||||||
|
35 6 4 4 2 1 46 3 4 0 2
|
||||||
|
0 1 3 4 5 2 22 6 4 0 1
|
||||||
|
0 7 5 1 6 5 68 3 4 1 -3
|
||||||
|
0 2 5 2 6 5 38 3 4 1 -2
|
||||||
|
33 7 4 3 6 3 69 2 4 0 1
|
||||||
|
270 2 5 4 3 0 67 3 4 0 1
|
||||||
|
45 7 2 4 6 0 88 4 4 0 2
|
||||||
|
40 3 6 2 5 5 68 3 4 1 -3
|
||||||
|
6 1 5 2 4 2 76 3 4 1 -2
|
||||||
|
2 7 4 4 6 0 72 2 4 0 2
|
||||||
|
0 0 6 2 6 6 37 6 4 1 -4
|
||||||
|
35 3 4 2 6 0 69 3 4 0 0
|
||||||
|
83 0 2 4 6 0 33 6 4 0 2
|
||||||
|
3500 7 2 2 6 0 34 4 4 0 4
|
||||||
|
100 2 4 4 7 2 30 3 4 0 3
|
||||||
|
350 2 3 3 6 1 19 3 4 0 3
|
||||||
|
100 3 4 6 2 0 44 3 4 0 0
|
||||||
|
67 1 4 4 7 1 64 3 4 0 3
|
||||||
|
30 5 7 7 2 0 37 4 4 0 5
|
||||||
|
0 7 6 3 5 4 31 5 5 1 -2
|
||||||
|
0 0 6 1 5 4 88 4 5 1 -4
|
||||||
|
6 7 6 2 6 6 77 4 5 1 -4
|
||||||
|
350 1 4 5 6 5 30 6 5 0 1
|
||||||
|
400 1 2 3 7 1 32 4 5 0 4
|
||||||
|
15 7 6 2 6 6 59 1 5 1 -4
|
||||||
|
0 0 4 4 4 3 47 4 5 0 0
|
||||||
|
3 2 4 6 5 1 22 3 5 0 -1
|
||||||
|
22 5 4 2 6 2 55 3 5 0 0
|
||||||
|
64 2 2 1 3 0 24 2 5 0 0
|
||||||
|
32 5 3 7 4 1 65 1 5 0 -3
|
||||||
|
390 7 3 6 2 2 24 3 5 0 -2
|
||||||
|
0 7 3 4 5 3 30 3 5 0 1
|
||||||
|
0 7 4 5 2 3 73 3 5 0 1
|
||||||
|
59 5 3 3 5 1 73 5 5 0 2
|
||||||
|
0 6 4 3 6 2 91 1 5 0 1
|
||||||
|
35 7 3 2 5 0 71 2 5 0 1
|
||||||
|
0 2 6 4 5 4 34 4 5 1 -1
|
||||||
|
170 7 4 3 2 0 48 2 6 0 1
|
||||||
|
12 1 6 2 6 5 42 4 6 1 -4
|
||||||
|
40 4 6 5 4 0 72 2 6 0 1
|
||||||
|
31 2 3 4 6 6 20 4 6 1 2
|
||||||
|
31 7 2 2 7 0 22 4 6 0 5
|
||||||
|
1600 1 3 3 6 1 24 6 6 0 3
|
||||||
|
1 1 4 2 7 2 39 6 6 0 1
|
||||||
|
4 7 6 1 6 6 83 5 6 1 -5
|
||||||
|
190 0 6 2 6 6 39 3 6 1 -4
|
||||||
|
53 3 5 3 6 1 33 5 6 0 -1
|
||||||
|
31 7 4 3 6 1 53 3 6 1 1
|
||||||
|
16 7 5 3 6 5 82 3 6 1 -1
|
||||||
|
33 5 4 3 5 6 82 3 6 1 0
|
||||||
|
0 3 5 3 6 5 47 6 7 1 -1
|
||||||
|
0 3 4 2 7 4 68 3 7 0 1
|
||||||
|
0 7 4 3 5 0 84 6 7 0 0
|
||||||
|
27 2 6 1 6 5 35 5 7 1 -5
|
||||||
|
84 7 4 5 6 1 67 2 7 0 1
|
||||||
|
22 3 5 3 5 4 33 2 7 1 -2
|
||||||
|
0 3 3 3 5 0 49 7 7 0 2
|
||||||
|
3500 0 4 3 7 0 91 1 7 0 2
|
||||||
|
390 7 4 5 3 1 43 3 7 0 0
|
||||||
|
0 7 4 3 2 6 65 4 7 0 1
|
||||||
|
16 7 5 6 3 0 69 3 7 0 1
|
||||||
|
200 0 5 5 4 1 56 4 8 0 1
|
||||||
|
640 0 2 3 5 0 24 6 8 0 2
|
||||||
|
0 7 4 4 5 0 77 3 8 0 1
|
||||||
|
45 7 6 3 7 0 74 3 8 0 -2
|
||||||
|
12 0 7 3 6 6 25 6 8 1 -3
|
||||||
|
20 7 6 2 5 4 85 1 8 1 -3
|
||||||
|
7300 5 7 7 6 3 21 2 8 0 1
|
||||||
|
64 7 6 3 1 0 24 4 8 0 2
|
||||||
|
13 7 5 4 7 4 73 4 8 0 1
|
||||||
|
190 0 4 5 3 2 37 3 8 0 0
|
||||||
|
9 4 4 5 1 2 35 4 8 0 2
|
||||||
|
0 7 4 4 7 0 47 3 8 0 3
|
||||||
|
170 2 4 2 6 6 21 3 8 1 0
|
||||||
|
640 7 3 6 4 0 55 5 8 0 -2
|
||||||
|
9 4 6 3 6 6 30 6 8 1 -3
|
||||||
|
0 4 5 3 6 4 76 7 8 1 -1
|
||||||
|
7300 5 3 4 3 3 36 4 8 0 -1
|
||||||
|
2800 0 1 1 7 0 38 3 9 0 6
|
||||||
|
0 7 2 3 5 0 67 3 9 0 2
|
||||||
|
30 7 7 3 7 6 70 2 9 1 -4
|
||||||
|
44 7 5 3 7 2 78 4 9 0 0
|
||||||
|
7300 1 2 2 7 3 27 6 9 0 5
|
||||||
|
330 4 3 5 6 1 51 4 9 0 1
|
||||||
|
3 0 6 7 3 5 33 4 9 0 2
|
||||||
|
51 2 6 1 5 6 80 6 9 1 -4
|
||||||
|
29 5 4 1 6 1 79 1 9 0 -1
|
||||||
|
630 2 6 4 5 4 66 1 9 1 -1
|
||||||
|
170 0 4 1 6 0 32 3 10 0 -1
|
||||||
|
33 7 4 5 7 0 70 2 10 0 2
|
||||||
|
0 3 2 3 6 3 42 3 10 0 3
|
||||||
|
9 5 5 4 5 5 73 4 10 1 -1
|
||||||
|
22 4 4 4 6 0 87 2 10 0 2
|
||||||
|
100 0 7 5 1 1 30 5 10 0 4
|
||||||
|
2 2 4 4 5 3 52 3 10 0 1
|
||||||
|
0 6 5 3 6 1 62 4 10 0 -1
|
||||||
|
50 7 6 3 4 0 67 3 10 0 -1
|
||||||
|
15 4 6 3 4 4 37 6 10 0 -1
|
||||||
|
3 4 3 5 7 0 37 4 10 0 2
|
||||||
|
720 5 1 5 6 1 64 6 10 0 1
|
||||||
|
640 7 1 1 5 0 34 3 10 0 4
|
||||||
|
5 7 4 4 7 0 70 3 10 0 3
|
||||||
|
24 2 6 2 6 6 31 5 10 1 -4
|
||||||
|
22 7 2 2 6 0 29 6 11 0 4
|
||||||
|
55 7 4 5 4 1 71 2 11 0 -1
|
||||||
|
0 2 4 4 4 0 67 1 11 0 0
|
||||||
|
1600 5 4 4 6 0 41 7 11 0 2
|
||||||
|
170 6 1 2 6 0 49 6 11 0 4
|
||||||
|
1000 7 4 4 5 0 42 5 11 0 1
|
||||||
|
63 0 6 3 2 0 78 2 11 0 1
|
||||||
|
110 0 4 1 6 1 24 3 11 0 -1
|
||||||
|
16 7 4 6 6 1 29 3 11 0 0
|
||||||
|
100 3 4 2 6 5 39 5 11 1 0
|
||||||
|
7300 3 5 3 6 1 19 4 11 0 -1
|
||||||
|
22 2 4 2 7 1 32 5 11 0 1
|
||||||
|
71 3 4 2 6 5 69 3 11 1 0
|
||||||
|
900 4 5 2 5 5 83 3 11 1 -3
|
||||||
|
35 7 4 1 5 4 76 2 11 1 -2
|
||||||
|
2 7 7 1 2 0 62 2 11 0 -1
|
||||||
|
83 2 3 3 6 0 47 7 11 0 3
|
||||||
|
370 5 6 7 4 0 35 3 11 0 1
|
||||||
|
12 0 4 5 3 4 23 3 11 0 0
|
||||||
|
370 7 4 4 1 1 79 4 11 0 3
|
||||||
|
100 7 6 2 6 5 64 5 11 1 -4
|
||||||
|
470 7 6 2 4 5 70 4 11 1 -2
|
||||||
|
22 7 6 1 6 6 87 5 11 1 -5
|
||||||
|
2800 0 3 6 1 0 28 2 12 0 -1
|
||||||
|
47 5 3 5 7 1 58 3 12 0 2
|
||||||
|
900 5 4 4 6 1 85 2 12 0 2
|
||||||
|
330 7 3 6 4 0 62 3 12 0 -2
|
||||||
|
84 0 3 2 7 1 26 6 12 0 3
|
||||||
|
0 0 6 2 5 5 28 3 12 1 -3
|
||||||
|
33 3 6 1 7 6 88 2 12 1 -4
|
||||||
|
53 7 2 3 6 0 57 6 12 0 3
|
||||||
|
8 7 2 2 6 0 78 3 12 0 4
|
||||||
|
2 7 4 4 2 0 56 3 12 0 2
|
||||||
|
0 0 4 6 3 3 46 5 12 0 -1
|
||||||
|
0 2 4 4 3 5 20 3 12 1 1
|
||||||
|
0 0 5 6 4 1 24 4 12 0 0
|
||||||
|
0 7 5 2 6 2 72 4 12 0 -2
|
||||||
|
15 7 2 4 7 1 51 4 12 0 3
|
||||||
|
900 0 6 2 5 6 34 6 12 1 -3
|
||||||
|
30 2 4 2 6 1 21 4 12 0 0
|
||||||
|
0 7 4 4 6 2 74 7 12 0 2
|
||||||
|
170 3 4 4 6 1 48 1 12 0 2
|
||||||
|
900 2 3 3 7 5 28 3 12 0 4
|
||||||
|
0 6 7 1 7 5 38 2 12 1 -6
|
||||||
|
1600 7 4 6 1 0 70 3 12 0 1
|
||||||
|
0 7 4 5 4 0 72 2 12 0 -1
|
||||||
|
2800 0 4 5 6 0 41 3 12 0 1
|
||||||
|
110 5 3 4 5 1 50 7 12 0 1
|
||||||
|
1 7 6 2 5 5 73 3 12 1 -3
|
||||||
|
3 5 5 2 4 0 79 6 12 0 -2
|
||||||
|
0 4 5 1 4 5 76 2 12 1 -3
|
||||||
|
22 0 5 3 5 5 62 5 12 1 -2
|
||||||
|
63 3 6 2 6 6 30 6 12 1 -4
|
||||||
|
290 0 6 3 6 5 35 4 12 1 -3
|
||||||
|
2 7 1 2 7 1 66 4 12 0 5
|
||||||
|
40 0 2 4 6 0 35 4 12 0 2
|
||||||
|
67 0 6 1 5 6 57 6 12 1 -4
|
||||||
|
0 5 4 5 4 5 37 5 12 1 -1
|
||||||
|
470 7 5 5 2 1 61 3 13 0 3
|
||||||
|
0 7 6 2 6 6 56 3 13 1 -4
|
||||||
|
4 6 3 4 5 1 53 3 13 0 1
|
||||||
|
20 0 4 5 3 2 24 6 13 1 0
|
||||||
|
2800 7 4 1 6 5 74 3 13 1 -1
|
||||||
|
0 0 4 4 3 1 36 3 13 0 1
|
||||||
|
1 0 6 2 4 5 30 5 13 1 -2
|
||||||
|
640 0 4 7 4 1 55 2 13 0 -3
|
||||||
|
170 3 3 2 7 2 35 6 13 0 3
|
||||||
|
270 2 3 4 6 0 26 4 13 0 2
|
||||||
|
390 0 3 4 6 2 25 4 13 0 2
|
||||||
|
16 2 6 7 4 3 27 3 13 0 1
|
||||||
|
11 7 4 1 6 5 66 3 13 1 -1
|
||||||
|
0 1 5 2 6 2 39 2 13 0 -2
|
||||||
|
270 7 1 1 2 2 58 5 13 0 1
|
||||||
|
170 2 4 4 4 0 53 3 13 1 0
|
||||||
|
900 7 6 7 4 0 76 3 13 0 1
|
||||||
|
270 7 5 2 7 1 51 3 13 0 -1
|
||||||
|
0 7 4 2 7 0 70 2 13 0 1
|
||||||
|
350 3 6 3 6 6 68 4 13 1 -3
|
||||||
|
0 0 5 4 5 2 32 3 13 1 -1
|
||||||
|
6 0 5 4 5 5 55 3 13 0 -1
|
||||||
|
290 7 2 2 6 0 52 4 13 0 4
|
||||||
|
630 7 6 4 6 4 73 2 13 1 -2
|
||||||
|
900 0 5 4 7 0 42 2 13 0 1
|
||||||
|
31 2 4 4 3 4 23 5 13 1 1
|
||||||
|
1600 5 2 3 6 0 30 7 14 0 3
|
||||||
|
71 7 2 2 7 0 68 4 14 0 5
|
||||||
|
200 7 5 2 3 2 68 3 14 0 -1
|
||||||
|
0 0 6 4 7 3 68 6 14 0 -1
|
||||||
|
30 5 2 3 6 0 38 5 14 0 3
|
||||||
|
10 1 4 3 6 2 74 3 14 0 1
|
||||||
|
0 7 5 6 3 0 59 2 14 0 1
|
||||||
|
900 2 5 2 5 2 73 2 14 0 -3
|
||||||
|
71 7 2 3 6 0 79 3 14 0 3
|
||||||
|
22 3 7 1 6 5 28 4 14 1 -5
|
||||||
|
0 7 6 2 6 6 50 3 14 1 -4
|
||||||
|
0 4 6 3 1 1 36 4 14 0 2
|
||||||
|
0 3 6 2 6 6 50 3 14 1 -4
|
||||||
|
0 1 6 2 6 6 61 3 14 1 -4
|
||||||
|
7300 3 2 2 6 0 37 4 14 0 4
|
||||||
|
83 0 3 4 7 0 29 6 14 0 3
|
||||||
|
93 7 2 3 7 0 39 4 14 0 4
|
||||||
|
0 7 4 5 4 2 83 6 14 1 -1
|
||||||
|
51 7 6 1 5 4 68 6 14 1 -4
|
||||||
|
31 2 6 1 5 6 25 4 14 1 -4
|
||||||
|
93 2 1 3 6 1 41 6 14 0 3
|
||||||
|
0 7 3 2 6 1 67 3 14 0 2
|
||||||
|
0 3 4 2 6 4 36 6 14 1 0
|
||||||
|
31 4 6 2 6 4 66 4 14 1 -4
|
||||||
|
900 1 3 2 7 1 55 4 14 0 3
|
||||||
|
0 4 3 2 6 2 42 5 14 0 2
|
||||||
|
2 7 6 3 5 5 42 3 14 1 -2
|
||||||
|
110 3 4 5 7 1 36 3 14 0 2
|
||||||
|
63 1 6 4 6 4 53 5 14 1 -2
|
||||||
|
900 0 3 2 5 1 36 5 14 0 1
|
||||||
|
31 3 4 3 6 2 29 7 14 0 1
|
||||||
|
510 1 4 4 6 0 31 3 14 0 2
|
||||||
|
270 2 3 4 6 1 43 6 14 0 2
|
||||||
|
9 3 3 4 7 1 33 6 14 0 3
|
||||||
|
3 1 6 6 2 0 63 3 14 0 4
|
||||||
|
29 1 5 2 4 2 25 5 14 0 -2
|
||||||
|
45 2 3 2 6 0 72 4 14 0 2
|
||||||
|
83 5 3 3 6 1 40 4 14 0 3
|
||||||
|
22 7 4 2 6 2 27 6 14 0 0
|
||||||
|
15 3 5 4 7 2 26 4 15 0 1
|
||||||
|
110 7 5 2 5 6 67 3 15 1 -3
|
||||||
|
8 0 4 5 5 1 21 3 15 0 0
|
||||||
|
11 5 6 2 6 6 27 7 15 1 -4
|
||||||
|
56 4 6 5 3 0 78 6 15 1 2
|
||||||
|
8 3 4 4 3 1 32 3 15 0 1
|
||||||
|
100 5 4 2 6 1 68 4 15 0 0
|
||||||
|
900 7 4 2 6 5 76 4 15 0 0
|
||||||
|
67 7 3 3 6 0 33 5 15 0 3
|
||||||
|
35 4 6 2 5 4 38 2 15 1 -3
|
||||||
|
35 3 4 3 5 1 49 7 15 0 0
|
||||||
|
22 0 5 4 6 5 61 4 15 1 0
|
||||||
|
110 7 7 1 5 4 57 3 15 1 -4
|
||||||
|
12 0 4 2 5 2 20 3 15 0 -1
|
||||||
|
7300 7 4 2 4 4 63 6 15 1 -2
|
||||||
|
0 0 4 1 5 4 53 3 15 1 -2
|
||||||
|
19 5 4 2 6 5 35 4 15 1 0
|
||||||
|
470 3 4 4 7 0 39 3 15 0 3
|
||||||
|
4 3 3 5 3 4 48 3 15 1 -2
|
||||||
|
640 4 6 2 5 4 62 3 15 1 -3
|
||||||
|
640 2 4 2 3 1 30 5 15 0 -1
|
||||||
|
200 7 7 1 4 6 26 6 15 1 -3
|
||||||
|
0 7 3 2 4 5 74 6 15 1 0
|
||||||
|
29 4 3 4 6 2 37 5 15 0 2
|
||||||
|
330 2 4 5 5 1 43 5 15 0 0
|
||||||
|
19 5 5 1 4 6 68 3 15 1 -3
|
||||||
|
1 7 5 3 4 5 73 5 15 1 -1
|
||||||
|
110 5 6 1 6 6 60 7 15 1 -5
|
||||||
|
0 7 5 4 6 1 35 3 15 0 0
|
||||||
|
350 4 3 4 6 0 29 6 15 0 2
|
||||||
|
2 5 3 3 5 1 25 6 15 0 2
|
||||||
|
0 7 2 2 6 0 25 7 15 0 4
|
||||||
|
7 4 2 4 5 0 70 6 15 0 1
|
||||||
|
71 1 6 2 5 6 41 3 15 1 -3
|
||||||
|
53 0 4 2 6 1 37 6 15 0 0
|
||||||
|
0 0 6 1 6 6 39 5 15 1 -5
|
||||||
|
2 6 3 4 6 0 35 4 15 0 2
|
||||||
|
190 4 2 3 6 0 62 7 15 0 3
|
||||||
|
31 0 3 2 6 1 30 7 15 0 2
|
||||||
|
16 7 4 2 6 0 74 4 15 0 0
|
||||||
|
22 7 3 3 4 5 47 3 15 0 1
|
||||||
|
3 4 4 5 3 1 43 6 15 0 0
|
||||||
|
0 6 5 3 6 5 64 2 15 1 -1
|
||||||
|
0 7 4 3 5 2 75 4 15 1 0
|
||||||
|
67 3 4 4 7 1 27 6 15 0 3
|
||||||
|
40 7 4 4 6 0 21 3 15 0 2
|
||||||
|
74 4 2 2 6 1 70 2 15 0 4
|
||||||
|
3 6 5 2 6 5 67 3 15 1 -2
|
||||||
|
140 7 6 4 5 6 82 5 15 0 -1
|
||||||
|
14 0 2 2 6 0 40 6 15 0 4
|
||||||
|
110 0 5 3 6 5 26 4 15 1 -1
|
||||||
|
35 3 4 3 5 1 29 6 15 0 0
|
||||||
|
0 1 4 5 6 2 28 6 15 0 1
|
||||||
|
310 7 6 4 3 5 65 3 15 0 1
|
||||||
|
900 2 6 5 3 1 25 3 15 0 2
|
||||||
|
0 7 3 2 7 1 65 2 15 0 3
|
||||||
|
11 4 6 2 6 5 38 5 15 1 -4
|
||||||
|
0 2 4 3 5 5 72 7 15 1 0
|
||||||
|
270 7 3 2 7 1 67 3 15 0 3
|
||||||
|
51 7 3 1 7 0 74 3 15 0 2
|
||||||
|
11 5 2 4 4 2 71 6 15 0 0
|
||||||
|
2 0 4 6 4 0 47 3 15 0 -2
|
||||||
|
20 7 5 4 6 1 69 1 15 0 0
|
||||||
|
31 3 3 3 6 1 29 6 15 0 3
|
||||||
|
2 0 6 2 5 6 34 3 15 1 -3
|
||||||
|
5 3 4 3 6 0 43 4 15 0 1
|
||||||
|
22 7 2 1 7 0 30 3 15 0 4
|
||||||
|
0 7 5 5 4 0 76 2 15 0 1
|
||||||
|
27 0 2 4 6 1 26 5 16 0 2
|
||||||
|
7 7 4 3 6 2 76 5 16 0 1
|
||||||
|
0 0 1 4 6 1 42 7 16 0 2
|
||||||
|
0 4 3 1 4 5 33 3 16 1 -1
|
||||||
|
0 1 6 2 5 5 25 3 16 1 -3
|
||||||
|
2800 0 2 2 7 0 51 4 16 0 5
|
||||||
|
0 4 4 2 5 4 57 3 16 1 -1
|
||||||
|
22 1 6 1 5 6 21 4 16 1 -4
|
||||||
|
9 7 5 1 4 5 79 7 16 1 -3
|
||||||
|
0 1 6 2 5 5 35 5 16 1 -3
|
||||||
|
9 5 4 2 5 4 57 6 16 1 -1
|
||||||
|
0 1 4 4 6 5 32 6 16 0 2
|
||||||
|
37 5 4 5 5 2 51 6 16 0 0
|
||||||
|
23 0 5 2 4 6 62 7 16 1 -2
|
||||||
|
0 4 5 1 6 5 48 4 16 1 -3
|
||||||
|
0 7 7 1 6 6 39 3 16 1 -5
|
||||||
|
0 5 3 2 6 5 26 6 16 1 2
|
||||||
|
40 0 4 2 4 1 38 3 16 1 -2
|
||||||
|
0 5 3 3 4 0 50 4 16 0 1
|
||||||
|
9 2 5 5 6 4 33 3 16 1 1
|
||||||
|
15 5 4 3 6 2 36 3 16 0 1
|
||||||
|
640 5 4 4 6 0 24 6 16 0 2
|
||||||
|
0 4 3 2 6 0 25 5 16 0 2
|
||||||
|
0 7 6 5 3 0 62 3 16 0 2
|
||||||
|
0 2 4 3 3 6 33 3 16 1 0
|
||||||
|
0 7 6 2 5 6 53 6 16 1 -3
|
||||||
|
22 7 6 2 5 6 68 6 16 1 -3
|
||||||
|
22 7 5 4 6 1 68 3 16 0 0
|
||||||
|
10 1 6 1 5 5 38 3 16 1 -4
|
||||||
|
29 1 4 5 3 0 58 1 16 0 0
|
||||||
|
170 7 4 2 6 5 34 6 16 1 0
|
||||||
|
4 2 4 3 4 1 58 2 16 0 -1
|
||||||
|
11 0 3 4 7 1 35 5 16 0 3
|
||||||
|
31 3 7 2 6 6 42 6 16 1 -4
|
||||||
|
0 7 4 4 6 0 54 3 16 0 2
|
||||||
|
0 7 6 2 5 6 69 3 16 1 -3
|
||||||
|
360 2 4 6 5 0 35 4 16 0 -1
|
||||||
|
0 7 6 1 5 6 66 4 16 1 -4
|
||||||
|
900 2 3 2 7 0 58 3 16 0 3
|
||||||
|
51 5 2 4 7 1 41 3 16 0 3
|
||||||
|
0 2 5 2 6 4 35 6 16 0 -2
|
||||||
|
110 0 3 3 6 1 40 7 16 0 3
|
||||||
|
1 7 4 7 2 0 53 1 16 0 -1
|
||||||
|
8 5 6 2 6 5 67 6 16 1 -4
|
||||||
|
5 5 4 4 6 0 32 5 16 0 2
|
||||||
|
87 4 3 4 6 0 41 4 16 0 2
|
||||||
|
3 1 2 1 6 1 43 7 16 0 3
|
||||||
|
51 1 5 3 2 0 65 2 16 0 1
|
||||||
|
350 7 3 3 7 0 60 5 16 0 4
|
||||||
|
3 7 5 3 6 0 77 6 16 0 -1
|
||||||
|
630 0 6 5 4 1 35 4 16 0 1
|
||||||
|
180 4 6 7 5 1 48 4 16 0 0
|
||||||
|
0 0 6 2 6 6 52 3 16 1 -4
|
||||||
|
35 7 5 3 6 2 43 7 16 0 -1
|
||||||
|
0 7 6 2 6 6 43 5 16 1 -4
|
||||||
|
0 7 6 2 6 6 67 4 16 1 -4
|
||||||
|
6 1 4 4 7 0 56 3 16 0 3
|
||||||
|
7300 2 3 3 4 0 62 4 16 0 1
|
||||||
|
2 1 7 7 5 0 62 3 16 0 2
|
||||||
|
35 3 3 2 6 1 22 6 16 0 2
|
||||||
|
0 0 2 2 6 1 21 5 16 0 4
|
||||||
|
45 3 6 1 6 6 34 3 16 1 -5
|
||||||
|
0 7 4 3 5 0 70 3 16 0 0
|
||||||
|
5 1 3 2 6 2 50 3 16 0 2
|
||||||
|
35 5 6 1 6 6 42 4 16 1 -5
|
||||||
|
900 5 6 2 6 6 73 3 16 1 -4
|
||||||
|
35 2 3 1 6 0 57 7 16 0 1
|
||||||
|
0 7 6 1 5 6 40 6 16 1 -4
|
||||||
|
0 2 6 1 6 6 58 6 16 1 -5
|
||||||
|
11 7 4 6 2 1 62 3 16 0 0
|
||||||
|
40 1 3 5 3 1 44 3 17 0 -2
|
||||||
|
0 3 5 3 7 6 30 6 17 0 0
|
||||||
|
23 7 3 3 7 0 76 5 17 0 4
|
||||||
|
270 3 4 3 3 1 50 4 17 0 0
|
||||||
|
9 1 6 2 5 5 41 4 17 1 -3
|
||||||
|
0 6 6 3 6 6 77 3 17 1 -3
|
||||||
|
0 3 6 3 5 6 35 6 17 1 -2
|
||||||
|
0 7 4 3 4 3 39 4 17 0 -1
|
||||||
|
2 0 4 2 4 4 72 3 17 1 -2
|
||||||
|
0 2 6 2 5 6 42 7 17 1 -3
|
||||||
|
16 7 5 2 6 5 85 2 17 1 -2
|
||||||
|
7300 0 4 2 6 3 79 4 17 0 0
|
||||||
|
0 5 2 3 6 0 39 4 17 0 3
|
||||||
|
23 4 6 1 5 6 58 6 17 1 -4
|
||||||
|
42 0 4 2 6 5 27 6 17 1 0
|
||||||
|
2 4 4 3 5 1 43 5 17 0 0
|
||||||
|
0 0 3 3 6 0 58 4 17 0 3
|
||||||
|
42 7 4 3 6 4 28 7 17 1 1
|
||||||
|
470 2 5 3 6 5 27 6 17 0 -1
|
||||||
|
42 0 3 6 5 2 40 3 17 0 -1
|
||||||
|
0 5 1 3 6 0 43 6 17 0 3
|
||||||
|
40 7 5 4 6 5 64 3 17 1 0
|
||||||
|
180 7 2 2 6 0 39 6 17 0 4
|
||||||
|
110 7 4 2 6 4 76 4 17 1 0
|
||||||
|
140 5 3 3 6 0 64 5 17 0 3
|
||||||
|
0 4 4 6 3 2 28 4 17 0 -1
|
||||||
|
0 2 4 2 6 1 45 3 17 0 0
|
||||||
|
190 1 6 2 6 6 22 3 17 1 -4
|
||||||
|
35 1 3 3 6 1 27 6 17 0 3
|
||||||
|
45 0 1 2 7 0 31 4 17 0 5
|
||||||
|
170 1 2 2 7 0 34 6 17 0 5
|
||||||
|
0 4 2 3 7 0 30 4 17 0 4
|
||||||
|
0 2 6 1 6 6 64 3 17 1 -5
|
||||||
|
35 0 6 1 6 4 36 6 17 1 -5
|
||||||
|
5 7 4 3 5 4 31 2 17 1 0
|
||||||
|
350 1 4 3 4 5 37 3 17 1 -1
|
||||||
|
4 0 6 4 2 1 48 3 17 0 2
|
||||||
|
70 0 5 7 7 3 41 3 17 0 0
|
||||||
|
8 2 5 2 6 5 25 4 17 1 -2
|
||||||
|
12 5 4 2 6 6 82 3 17 1 0
|
||||||
|
5 1 6 2 5 4 36 7 17 1 -3
|
||||||
|
0 7 6 2 5 1 47 3 17 1 -3
|
||||||
|
16 6 2 3 6 0 67 4 17 0 3
|
||||||
|
0 0 4 6 2 5 24 4 17 1 0
|
||||||
|
9 0 4 5 3 5 33 6 17 1 0
|
||||||
|
14 7 4 1 5 1 59 4 17 1 -2
|
||||||
|
22 7 7 1 6 6 71 4 17 1 -5
|
||||||
|
0 7 6 2 5 6 36 3 17 1 -3
|
||||||
|
1 3 3 3 5 0 41 3 17 0 2
|
||||||
|
1600 5 6 2 6 6 38 6 17 1 -4
|
||||||
|
7300 1 6 3 6 6 32 3 17 1 -3
|
||||||
|
19 0 5 6 7 1 32 4 17 0 1
|
||||||
|
9 6 5 4 6 0 75 6 17 0 0
|
||||||
|
0 7 5 2 4 4 52 3 17 1 -2
|
||||||
|
1600 5 2 3 6 0 29 7 17 0 3
|
||||||
|
12 7 3 4 6 0 71 3 17 0 2
|
||||||
|
1 0 6 2 5 2 33 6 17 0 -3
|
||||||
|
0 3 1 2 7 2 67 7 17 0 5
|
||||||
|
0 2 6 1 6 6 49 4 17 1 -5
|
||||||
|
0 0 2 3 5 1 31 7 17 0 2
|
||||||
|
9 7 3 4 6 1 53 2 17 0 2
|
||||||
|
0 3 2 3 7 0 35 7 17 0 4
|
||||||
|
170 7 6 2 6 6 49 7 18 1 -4
|
||||||
|
3 7 6 4 4 0 74 3 18 0 0
|
||||||
|
14 0 6 1 5 6 29 4 18 1 -4
|
||||||
|
1 5 2 3 6 0 27 6 18 0 3
|
||||||
|
23 5 4 4 6 5 70 4 18 0 2
|
||||||
|
0 0 6 1 6 6 43 6 18 1 -5
|
||||||
|
1 5 2 3 6 2 47 7 18 0 3
|
||||||
|
0 4 4 4 6 0 42 7 18 0 2
|
||||||
|
9 0 6 2 5 6 41 6 18 1 -3
|
||||||
|
130 2 6 4 6 6 37 4 18 1 -2
|
||||||
|
5 5 5 5 4 1 61 3 18 0 1
|
||||||
|
4 7 4 2 6 0 84 4 18 0 0
|
||||||
|
2800 7 5 3 7 0 46 6 18 0 0
|
||||||
|
4 0 4 3 6 5 39 7 18 0 1
|
||||||
|
6 7 2 3 6 0 53 3 18 0 3
|
||||||
|
4 7 5 1 6 6 87 6 18 1 -3
|
||||||
|
84 2 4 6 2 0 51 3 18 0 0
|
||||||
|
0 0 5 2 5 4 37 5 18 1 -3
|
||||||
|
9 1 4 3 5 4 37 6 18 1 0
|
||||||
|
290 0 5 2 6 6 40 6 18 1 -2
|
||||||
|
0 3 4 4 6 2 46 6 18 0 2
|
||||||
|
9 7 1 4 1 3 44 3 18 1 -3
|
||||||
|
0 2 4 3 6 1 37 3 18 0 1
|
||||||
|
0 7 4 2 6 2 55 7 18 0 0
|
||||||
|
22 0 5 2 5 1 40 6 18 1 -3
|
||||||
|
0 5 3 2 6 1 42 7 18 0 2
|
||||||
|
640 7 6 2 6 6 40 3 18 1 -4
|
||||||
|
10 0 6 2 6 6 32 3 18 1 -4
|
||||||
|
4 5 5 2 5 4 58 7 18 1 -3
|
||||||
|
0 2 5 1 6 4 37 3 18 1 -3
|
||||||
|
640 4 4 5 5 5 51 3 18 1 0
|
||||||
|
110 5 3 5 4 5 44 5 18 0 -1
|
||||||
|
0 2 4 5 2 0 23 2 18 0 1
|
||||||
|
0 2 6 2 6 6 38 3 18 1 -4
|
||||||
|
67 0 3 4 6 0 42 7 18 0 2
|
||||||
|
520 7 4 2 5 4 60 4 18 1 -1
|
||||||
|
0 3 4 3 6 1 34 4 18 0 1
|
||||||
|
51 1 6 2 6 6 49 3 18 1 -4
|
||||||
|
0 0 4 2 3 4 40 3 18 1 -1
|
||||||
|
2800 7 2 2 7 0 23 4 18 0 5
|
||||||
|
110 2 2 3 6 0 47 3 18 0 3
|
||||||
|
7300 7 4 4 7 0 44 5 18 0 3
|
||||||
|
7300 5 5 2 6 1 37 7 18 1 -2
|
||||||
|
0 2 6 1 6 5 26 5 18 1 -5
|
||||||
|
16 7 7 6 5 2 51 4 18 0 1
|
||||||
|
140 4 4 4 6 0 69 3 18 0 2
|
||||||
|
54 2 2 2 7 0 36 6 18 0 5
|
||||||
|
2 2 2 3 6 2 28 4 18 0 3
|
||||||
|
470 4 5 2 6 5 40 4 19 1 -2
|
||||||
|
0 1 6 1 6 5 45 4 19 1 -5
|
||||||
|
75 3 5 2 5 5 75 6 19 1 -3
|
||||||
|
11 2 6 2 5 5 32 6 19 1 -3
|
||||||
|
0 0 3 2 6 1 27 3 19 0 2
|
||||||
|
2800 6 5 2 5 3 63 6 19 1 -3
|
||||||
|
140 1 5 3 6 6 79 7 19 1 -1
|
||||||
|
9 7 3 2 6 1 40 6 19 0 2
|
||||||
|
7300 2 4 3 6 1 57 6 19 0 1
|
||||||
|
45 3 3 4 5 1 44 6 19 0 1
|
||||||
|
1600 7 5 2 5 6 78 6 19 1 -3
|
||||||
|
9 7 4 5 5 5 58 7 19 0 0
|
||||||
|
0 7 2 2 7 1 55 3 19 0 5
|
||||||
|
130 4 3 4 6 2 25 6 19 0 2
|
||||||
|
9 0 2 3 6 0 27 7 19 0 3
|
||||||
|
3500 3 4 2 7 2 21 3 19 0 1
|
||||||
|
0 3 6 2 6 5 37 4 19 1 -4
|
||||||
|
0 0 7 3 4 5 31 3 19 1 -1
|
||||||
|
35 7 2 4 6 0 85 7 19 0 2
|
||||||
|
900 1 4 2 6 5 33 3 19 0 0
|
||||||
|
27 7 3 7 4 3 63 2 19 1 -3
|
||||||
|
900 7 4 4 6 1 51 3 19 0 2
|
||||||
|
0 2 3 5 2 0 46 3 19 0 -1
|
||||||
|
64 2 5 2 6 6 26 5 19 1 -2
|
||||||
|
4 7 3 3 6 0 57 6 19 0 3
|
||||||
|
0 3 4 2 6 1 48 3 19 1 0
|
||||||
|
0 2 7 1 5 5 55 3 19 1 -4
|
||||||
|
53 0 3 6 4 1 37 5 19 0 -2
|
||||||
|
290 2 6 2 6 6 39 3 19 1 -4
|
||||||
|
15 2 2 3 4 1 24 3 19 1 1
|
||||||
|
0 0 6 3 6 6 30 6 19 1 -3
|
||||||
|
35 0 3 4 6 1 36 7 19 0 2
|
||||||
|
0 0 5 4 6 4 45 3 19 0 0
|
||||||
|
470 1 5 2 6 2 29 3 19 0 -2
|
||||||
|
16 7 6 3 6 5 41 4 19 1 -3
|
||||||
|
40 1 5 3 5 4 28 5 19 1 -2
|
||||||
|
290 7 2 2 7 0 35 4 19 0 5
|
||||||
|
4 7 2 3 6 0 46 4 19 0 3
|
||||||
|
1 7 1 2 7 1 55 4 19 0 5
|
||||||
|
140 3 2 3 6 6 65 4 19 0 3
|
||||||
|
110 1 4 2 6 1 22 4 19 0 0
|
||||||
|
0 1 4 4 6 1 41 4 19 0 2
|
||||||
|
900 1 2 3 6 2 43 7 19 0 3
|
||||||
|
0 2 4 3 6 0 49 4 19 0 1
|
||||||
|
0 4 4 2 5 4 35 7 19 1 -1
|
||||||
|
51 7 3 3 5 1 80 3 19 0 2
|
||||||
|
71 2 4 6 2 0 57 4 19 0 0
|
||||||
|
900 4 3 3 6 0 48 3 19 0 3
|
||||||
|
83 4 1 4 6 2 43 6 19 0 2
|
||||||
|
360 3 4 4 7 0 34 4 19 0 3
|
||||||
|
180 7 2 2 4 0 44 5 19 0 2
|
||||||
|
1 3 6 1 6 4 49 6 20 1 -5
|
||||||
|
22 7 5 2 7 2 43 7 20 0 -1
|
||||||
|
1 0 4 3 5 6 39 4 20 1 0
|
||||||
|
2 2 4 3 7 1 27 3 20 0 2
|
||||||
|
34 4 6 2 6 6 30 6 20 1 -4
|
||||||
|
33 2 6 2 6 4 33 6 20 1 -4
|
||||||
|
10 1 2 3 7 0 32 5 20 0 4
|
||||||
|
290 7 5 2 6 6 31 6 20 1 -2
|
||||||
|
0 2 5 3 7 2 27 5 20 0 0
|
||||||
|
76 3 2 2 6 0 36 4 20 0 4
|
||||||
|
20 7 3 2 6 2 45 5 20 0 2
|
||||||
|
0 5 4 6 5 2 51 3 20 0 -1
|
||||||
|
0 0 6 2 3 5 36 4 20 1 -1
|
||||||
|
0 7 6 4 6 4 47 7 20 1 -2
|
||||||
|
470 4 4 4 6 1 42 6 20 0 2
|
||||||
|
9 6 6 1 5 6 70 6 20 1 -4
|
||||||
|
0 0 3 3 6 3 40 4 20 0 3
|
||||||
|
0 7 2 5 7 0 38 3 20 0 2
|
||||||
|
8 0 5 1 6 4 29 3 20 1 -3
|
||||||
|
1 0 4 2 5 6 58 3 20 1 -1
|
||||||
|
1 7 4 6 1 2 28 3 20 0 1
|
||||||
|
7 1 6 3 6 5 30 4 20 1 -3
|
||||||
|
0 3 4 3 6 2 48 5 20 0 1
|
||||||
|
170 0 6 2 6 6 35 4 20 1 -4
|
||||||
|
270 3 6 7 6 0 38 3 20 0 -1
|
||||||
|
0 0 4 2 5 4 56 7 20 1 -1
|
||||||
|
13 4 5 3 6 5 62 4 20 1 -1
|
||||||
|
10 7 6 1 6 5 56 4 20 1 -5
|
||||||
|
22 0 6 2 5 6 32 3 20 1 -3
|
||||||
|
640 5 4 5 7 1 50 5 20 0 2
|
||||||
|
900 3 3 4 6 1 45 6 20 0 2
|
||||||
|
22 7 3 3 4 0 36 6 20 0 1
|
||||||
|
62 1 6 2 6 6 49 4 20 1 -4
|
||||||
|
110 2 7 1 6 6 31 6 20 1 -5
|
||||||
|
84 4 2 3 6 2 33 7 20 0 3
|
||||||
|
0 1 6 2 6 6 35 4 20 1 -4
|
||||||
|
13 2 4 2 5 4 73 4 20 1 -1
|
||||||
|
20 7 6 1 5 6 54 5 20 1 -4
|
||||||
|
0 3 4 2 7 1 48 3 20 0 1
|
||||||
|
16 3 6 2 6 4 65 4 20 1 -4
|
||||||
|
6 7 5 6 5 6 46 4 20 0 -1
|
||||||
|
12 0 2 2 6 0 45 7 20 0 4
|
||||||
|
0 7 6 2 5 6 65 7 20 1 -3
|
||||||
|
0 0 6 2 6 6 45 6 20 1 -4
|
||||||
|
170 7 4 1 5 4 89 7 20 1 -2
|
||||||
|
100 0 5 3 6 4 32 4 20 0 -1
|
||||||
|
5 4 6 4 4 1 38 4 20 0 0
|
||||||
|
3500 5 2 4 2 5 33 4 20 1 -2
|
||||||
|
71 4 6 2 5 6 49 7 20 1 -3
|
||||||
|
4 7 5 2 6 5 64 6 20 1 -2
|
||||||
|
7300 5 5 4 7 1 55 4 20 0 1
|
||||||
|
7 0 3 3 5 1 56 7 20 0 2
|
||||||
|
290 4 4 5 3 5 30 6 20 0 0
|
||||||
|
0 0 5 4 6 5 41 7 20 1 0
|
||||||
|
0 1 6 2 5 6 39 5 20 1 -3
|
||||||
|
520 7 4 4 6 4 34 3 20 0 2
|
||||||
|
430 5 4 5 7 1 53 3 20 0 2
|
||||||
|
9 2 3 3 6 2 40 6 20 0 3
|
||||||
|
40 0 3 3 6 1 48 6 20 0 3
|
||||||
|
2 7 6 5 5 5 59 4 20 0 0
|
||||||
|
75 7 6 2 4 4 54 4 20 1 -2
|
||||||
|
170 0 3 5 6 1 41 6 20 0 1
|
||||||
|
170 1 2 3 6 0 41 6 20 0 3
|
||||||
|
640 5 5 3 6 4 63 7 20 0 -1
|
||||||
|
2800 1 7 1 6 4 39 3 20 1 -5
|
||||||
|
9 3 7 2 5 4 46 3 20 0 -3
|
||||||
|
150 7 4 3 6 0 55 3 20 0 1
|
||||||
|
0 7 4 1 5 3 42 3 20 1 -2
|
||||||
|
0 7 6 3 6 4 58 2 20 1 -3
|
||||||
|
100 0 3 2 6 2 42 4 20 0 2
|
||||||
|
33 7 4 3 4 2 40 6 20 1 -1
|
||||||
|
310 7 5 2 5 1 56 6 20 1 -3
|
||||||
|
53 1 4 5 2 2 37 4 20 0 1
|
||||||
|
13 2 3 2 6 2 37 6 20 0 2
|
||||||
|
290 6 5 1 5 5 49 6 20 1 -4
|
||||||
|
310 7 6 1 6 6 63 4 20 1 -5
|
||||||
|
0 7 6 2 5 6 30 7 20 1 -3
|
||||||
|
54 7 4 2 6 4 62 5 20 1 0
|
||||||
|
1600 0 4 3 6 1 30 6 20 0 1
|
||||||
|
14 0 6 3 6 6 34 6 20 1 -3
|
||||||
|
25 7 6 3 6 5 41 4 20 1 -3
|
||||||
|
45 0 4 5 4 6 43 3 20 1 -1
|
||||||
|
20 2 5 2 6 4 33 5 20 0 -2
|
||||||
|
18 6 4 3 5 5 67 7 20 1 0
|
||||||
|
740 7 5 1 6 0 55 5 20 1 -3
|
||||||
|
9 6 2 2 6 0 33 5 20 0 4
|
||||||
|
5 0 6 1 5 5 61 4 20 1 -4
|
||||||
|
7300 7 4 1 6 4 45 3 20 0 -1
|
||||||
|
81 7 4 3 7 0 34 3 20 0 2
|
||||||
|
190 1 6 2 6 6 35 3 20 1 -4
|
||||||
|
51 1 4 4 5 2 50 6 20 0 1
|
||||||
|
7300 7 3 4 2 0 38 4 20 0 0
|
||||||
|
350 0 6 2 6 5 56 3 20 1 -4
|
||||||
|
27 2 4 1 6 5 31 3 20 1 -1
|
||||||
|
33 5 6 1 4 5 40 7 20 1 -3
|
||||||
|
50 4 3 2 6 0 44 3 20 0 2
|
||||||
|
1 7 6 5 2 2 39 4 20 0 3
|
||||||
|
11 1 4 3 6 1 45 3 20 0 1
|
||||||
|
51 5 3 3 6 0 72 6 20 0 3
|
||||||
|
160 7 2 3 5 1 44 7 20 0 2
|
||||||
|
16 7 1 3 6 0 61 7 21 0 3
|
||||||
|
110 6 6 2 6 6 34 7 21 1 -4
|
||||||
|
110 4 5 2 5 5 61 6 21 1 -3
|
||||||
|
13 6 3 3 7 1 67 4 21 0 4
|
||||||
|
220 7 4 1 4 6 38 6 21 1 -3
|
||||||
|
470 7 4 2 6 1 50 6 21 0 0
|
||||||
|
22 6 6 1 6 6 62 4 21 1 -5
|
||||||
|
9 2 6 3 6 5 36 6 21 1 -3
|
||||||
|
22 3 6 3 5 4 50 6 21 1 -2
|
||||||
|
190 2 4 2 7 1 30 6 21 0 1
|
||||||
|
100 5 6 2 6 6 59 5 21 1 -4
|
||||||
|
14 7 3 3 6 0 62 6 21 0 3
|
||||||
|
0 7 4 3 6 3 40 4 21 0 1
|
||||||
|
180 1 6 1 6 6 30 4 21 1 -5
|
||||||
|
3 6 3 2 6 2 47 3 21 0 2
|
||||||
|
51 2 5 2 6 1 41 6 21 0 -2
|
||||||
|
9 2 6 1 6 6 35 7 21 1 -5
|
||||||
|
0 7 5 2 6 6 45 4 21 1 -2
|
||||||
|
0 2 4 2 6 4 34 6 21 1 0
|
||||||
|
71 2 5 7 2 2 55 3 21 0 1
|
||||||
|
290 1 5 3 6 2 37 4 21 0 -1
|
||||||
|
45 3 5 2 4 4 61 7 21 1 -2
|
||||||
|
0 3 4 5 4 1 62 2 21 0 -1
|
||||||
|
26 1 5 2 6 6 54 6 21 1 -2
|
||||||
|
87 3 2 3 6 1 33 6 21 0 3
|
||||||
|
0 0 2 3 6 1 50 6 21 0 3
|
||||||
|
630 0 3 4 7 2 37 3 21 0 3
|
||||||
|
50 5 5 3 6 3 44 3 21 1 -1
|
||||||
|
35 7 4 3 4 3 78 3 21 0 -1
|
||||||
|
180 7 6 2 4 6 56 3 21 0 -2
|
||||||
|
32 0 2 3 4 2 29 6 21 0 1
|
||||||
|
0 7 3 2 5 4 52 6 21 0 1
|
||||||
|
51 1 6 4 5 1 31 3 21 0 -1
|
||||||
|
40 0 6 3 6 6 34 7 21 1 -3
|
||||||
|
0 2 5 3 6 5 31 7 21 1 -1
|
||||||
|
0 7 4 3 5 2 43 6 21 0 0
|
||||||
|
0 1 4 4 3 5 31 3 21 1 1
|
||||||
|
1 6 6 1 6 6 63 7 21 1 -5
|
||||||
|
7 2 2 3 7 4 38 4 21 0 4
|
||||||
|
0 2 6 2 6 6 31 5 21 1 -4
|
||||||
|
71 2 2 1 7 0 64 3 21 0 4
|
||||||
|
75 2 3 2 5 5 55 7 21 0 1
|
||||||
|
55 1 2 2 6 0 41 3 21 0 4
|
||||||
|
290 4 3 4 5 5 38 4 21 1 1
|
||||||
|
88 4 6 3 6 5 28 6 21 1 -3
|
||||||
|
0 7 4 3 5 5 42 5 21 1 0
|
||||||
|
16 7 2 4 3 1 43 4 21 0 -1
|
||||||
|
75 1 5 1 6 5 37 4 21 1 -3
|
||||||
|
220 1 4 2 6 3 47 5 21 0 0
|
||||||
|
3 5 5 1 6 5 52 7 21 1 -3
|
||||||
|
130 0 5 2 6 5 32 4 21 1 -2
|
||||||
|
0 5 4 4 3 2 29 3 21 0 1
|
||||||
|
110 2 6 2 5 4 56 3 21 1 -3
|
||||||
|
12 7 4 3 5 5 63 7 21 1 0
|
||||||
|
180 3 3 3 6 1 35 5 21 0 3
|
||||||
|
93 7 4 3 5 3 36 4 21 1 0
|
||||||
|
170 7 4 2 7 0 75 5 21 0 1
|
||||||
|
31 5 3 3 6 1 48 6 21 0 3
|
||||||
|
62 4 7 2 6 6 36 5 21 1 -4
|
||||||
|
30 4 4 3 6 2 34 6 21 0 1
|
||||||
|
66 7 4 3 7 1 35 5 21 0 2
|
||||||
|
3 3 5 2 5 6 50 4 21 1 -3
|
||||||
|
18 3 5 2 5 6 39 7 21 1 -3
|
||||||
|
350 5 5 4 6 5 70 7 21 1 0
|
||||||
|
71 7 4 2 5 6 76 3 21 1 -1
|
||||||
|
3500 5 5 2 5 5 35 6 21 1 -3
|
||||||
|
0 0 3 4 3 3 53 7 21 1 -1
|
||||||
|
360 6 5 2 6 4 46 6 21 1 -2
|
||||||
|
81 2 5 2 5 4 34 4 21 1 -3
|
||||||
|
350 5 4 4 6 5 69 4 21 0 2
|
||||||
|
190 1 5 2 5 1 32 3 21 0 -3
|
||||||
|
0 7 5 1 6 6 50 2 21 1 -3
|
||||||
|
290 1 5 3 6 6 35 6 21 1 -1
|
||||||
|
18 0 6 1 6 6 67 5 21 1 -5
|
||||||
|
11 3 5 5 6 5 47 3 21 0 1
|
||||||
|
2 2 6 3 5 4 50 6 21 1 -2
|
||||||
|
570 0 6 2 6 4 32 3 21 1 -4
|
||||||
|
310 3 5 4 6 2 58 5 21 0 0
|
||||||
|
1 7 3 2 7 0 49 7 21 0 3
|
||||||
|
0 2 6 3 6 1 43 4 21 1 -3
|
||||||
|
35 1 5 2 6 5 24 6 21 1 -2
|
||||||
|
22 7 5 3 4 2 58 7 21 0 -1
|
||||||
|
2 1 2 2 5 0 43 4 21 0 3
|
||||||
|
0 7 4 3 6 5 59 3 21 1 1
|
||||||
|
0 3 6 1 6 6 40 5 21 1 -5
|
||||||
|
310 0 5 2 7 0 35 4 21 0 -1
|
||||||
|
470 5 2 3 5 1 48 4 21 0 2
|
||||||
|
0 4 6 1 6 6 40 3 21 1 -5
|
||||||
|
270 3 3 2 7 0 48 3 21 0 3
|
||||||
|
110 0 2 4 6 0 47 7 21 0 2
|
||||||
|
50 3 6 4 1 1 23 3 21 0 3
|
||||||
|
0 0 6 1 6 6 38 3 21 1 -5
|
||||||
|
3 7 6 2 6 6 81 7 21 1 -4
|
||||||
|
31 5 3 3 6 1 48 6 21 0 3
|
||||||
|
22 7 5 6 2 4 52 3 21 0 2
|
||||||
|
83 2 4 1 6 3 24 6 21 1 -1
|
||||||
|
9 0 4 2 6 1 21 5 21 0 0
|
||||||
|
5 7 5 2 6 5 70 6 21 1 -2
|
||||||
|
0 7 7 1 7 6 24 6 21 1 -6
|
||||||
|
0 7 4 3 5 1 57 7 21 0 0
|
||||||
|
0 6 3 5 6 4 37 6 21 0 1
|
||||||
|
27 1 3 5 3 5 25 5 21 0 -2
|
||||||
|
110 1 6 5 1 1 33 3 21 0 4
|
||||||
|
0 7 4 6 3 0 45 3 22 0 -1
|
||||||
|
0 3 5 2 7 5 42 6 22 0 -1
|
||||||
|
350 3 3 3 6 2 47 6 22 0 3
|
||||||
|
0 7 6 1 6 6 51 7 22 1 -5
|
||||||
|
5 7 5 4 6 5 85 2 22 0 0
|
||||||
|
15 4 3 3 6 2 32 7 22 0 3
|
||||||
|
35 7 3 3 6 2 31 7 22 0 3
|
||||||
|
0 2 4 3 6 2 23 6 22 0 1
|
||||||
|
75 3 3 3 6 0 42 6 22 0 3
|
||||||
|
0 5 6 2 5 5 55 6 22 1 -3
|
||||||
|
16 7 6 2 6 6 45 6 22 1 -4
|
||||||
|
0 1 6 2 5 6 35 7 22 1 -3
|
||||||
|
0 0 2 4 6 0 45 6 22 0 2
|
||||||
|
0 0 3 3 5 2 42 3 22 0 2
|
||||||
|
4 1 6 2 6 5 37 4 22 1 -4
|
||||||
|
62 0 4 4 4 5 38 3 22 1 0
|
||||||
|
0 3 2 2 6 1 47 7 22 0 4
|
||||||
|
4 7 4 2 6 2 32 6 22 0 0
|
||||||
|
56 2 5 2 6 5 35 6 22 1 -2
|
||||||
|
2 6 4 2 6 5 38 4 22 1 0
|
||||||
|
0 0 4 4 4 3 40 3 22 0 0
|
||||||
|
75 7 6 2 5 6 62 6 22 1 -3
|
||||||
|
10 2 2 2 6 1 28 7 22 0 4
|
||||||
|
0 6 6 2 5 6 59 5 22 1 -3
|
||||||
|
0 2 1 2 6 0 25 6 22 0 4
|
||||||
|
220 2 4 2 6 6 31 6 22 1 0
|
||||||
|
0 1 7 2 6 6 45 4 22 1 -4
|
||||||
|
75 0 5 2 7 5 42 6 22 0 -1
|
||||||
|
0 3 2 2 5 1 56 7 22 0 3
|
||||||
|
140 5 3 3 6 1 47 7 22 0 3
|
||||||
|
290 1 6 2 5 5 38 6 22 1 -3
|
||||||
|
350 7 4 3 7 2 47 6 22 0 2
|
||||||
|
55 1 5 2 6 6 49 4 22 1 -2
|
||||||
|
31 3 3 3 6 2 29 7 22 0 3
|
||||||
|
17 7 2 2 6 0 57 6 22 0 4
|
||||||
|
51 4 6 3 5 6 68 3 22 1 -2
|
||||||
|
140 7 2 2 4 0 76 6 22 0 2
|
||||||
|
9 4 6 2 6 5 66 6 22 1 -4
|
||||||
|
0 7 5 1 6 4 59 4 22 1 -3
|
||||||
|
640 7 5 3 6 4 37 7 22 0 -1
|
||||||
|
32 2 5 2 6 6 38 6 22 1 -2
|
||||||
|
5 7 6 2 6 5 47 7 22 1 -4
|
||||||
|
8 1 5 2 4 5 36 7 22 1 -2
|
||||||
|
18 0 5 4 6 0 45 7 22 0 0
|
||||||
|
0 6 5 2 6 5 39 7 22 1 -2
|
||||||
|
0 1 3 2 6 1 34 6 22 0 2
|
||||||
|
0 3 6 6 4 0 49 4 22 0 2
|
||||||
|
31 2 6 2 6 5 36 6 22 1 -4
|
||||||
|
350 7 6 1 6 6 81 5 22 1 -5
|
||||||
|
20 1 5 2 6 4 29 4 22 1 -2
|
||||||
|
70 3 5 3 4 0 45 6 22 0 -1
|
||||||
|
31 3 5 2 6 5 21 4 22 1 -2
|
||||||
|
3 7 2 4 3 6 33 6 22 1 -1
|
||||||
|
9 7 4 4 2 2 44 3 23 0 2
|
||||||
|
59 1 2 2 6 4 52 7 23 0 4
|
||||||
|
27 2 3 5 7 1 38 4 23 0 2
|
||||||
|
51 4 2 3 6 2 44 5 23 0 3
|
||||||
|
9 7 6 2 6 6 87 7 23 1 -4
|
||||||
|
0 2 7 2 6 5 22 3 23 1 -4
|
||||||
|
88 0 3 3 5 2 32 6 23 1 2
|
||||||
|
67 0 4 5 6 4 69 3 23 0 1
|
||||||
|
29 2 6 2 5 6 49 6 23 1 -3
|
||||||
|
5 0 6 2 6 6 53 3 23 1 -4
|
||||||
|
0 0 6 1 6 5 44 6 23 1 -5
|
||||||
|
900 1 6 2 7 5 34 6 23 1 -3
|
||||||
|
18 1 6 1 5 5 55 7 23 1 -4
|
||||||
|
190 2 3 3 6 0 35 6 23 0 3
|
||||||
|
2 7 4 5 5 3 55 3 23 0 0
|
||||||
|
5 3 6 3 5 5 27 6 23 1 -2
|
||||||
|
56 3 4 4 5 1 26 6 23 0 1
|
||||||
|
75 7 4 2 6 5 54 6 23 0 0
|
||||||
|
56 0 5 2 6 5 42 4 23 1 -2
|
||||||
|
0 5 6 1 6 6 57 7 23 1 -5
|
||||||
|
0 0 7 1 4 6 54 6 23 1 -3
|
||||||
|
75 6 6 3 6 6 55 6 23 1 -3
|
||||||
|
1600 7 5 3 6 6 50 7 23 1 -1
|
||||||
|
15 0 5 5 6 1 57 7 23 0 1
|
||||||
|
19 3 5 3 7 3 46 4 23 0 0
|
||||||
|
16 7 6 2 6 6 53 6 23 1 -4
|
||||||
|
42 2 3 3 5 0 32 7 23 0 2
|
||||||
|
18 5 5 2 5 3 53 5 23 1 -3
|
||||||
|
0 3 3 3 6 1 39 6 23 0 3
|
||||||
|
310 1 5 2 5 4 47 6 23 1 -3
|
||||||
|
1600 7 5 2 4 2 57 4 23 0 -2
|
||||||
|
23 5 6 3 6 6 49 6 23 1 -3
|
||||||
|
20 1 5 4 6 5 31 5 23 0 0
|
||||||
|
51 5 5 3 5 5 43 6 23 1 -2
|
||||||
|
0 2 5 2 5 4 44 6 23 1 -3
|
||||||
|
0 4 4 3 6 0 39 6 23 0 1
|
||||||
|
0 2 6 1 6 5 49 4 23 1 -5
|
||||||
|
18 7 5 4 6 4 72 6 23 1 0
|
||||||
|
7300 7 5 2 6 5 50 6 23 1 -2
|
||||||
|
110 1 5 2 6 6 28 4 23 1 -2
|
||||||
|
0 0 5 2 7 3 48 7 23 1 -1
|
||||||
|
3500 1 3 4 7 1 32 6 23 0 3
|
||||||
|
720 7 5 5 5 1 63 4 23 0 0
|
||||||
|
9 4 4 5 6 5 36 4 23 1 1
|
||||||
|
47 7 6 3 6 6 36 6 23 1 -3
|
||||||
|
350 7 3 2 7 2 53 3 23 0 3
|
||||||
|
0 5 2 2 6 2 44 7 23 0 4
|
||||||
|
0 0 4 2 6 6 41 7 24 1 0
|
||||||
|
83 0 2 3 6 1 56 7 24 0 3
|
||||||
|
1 4 4 4 6 2 63 7 24 0 2
|
||||||
|
190 7 2 4 6 0 52 6 24 0 2
|
||||||
|
0 7 3 3 7 2 43 7 24 0 4
|
||||||
|
12 7 4 3 6 2 40 3 24 0 1
|
||||||
|
9 5 5 1 7 4 69 4 24 1 -2
|
||||||
|
23 7 2 2 6 0 49 7 24 0 4
|
||||||
|
9 1 3 3 6 2 65 7 24 0 3
|
||||||
|
18 5 6 1 6 5 53 7 24 1 -5
|
||||||
|
0 5 5 3 5 6 50 4 24 1 -2
|
||||||
|
12 3 2 4 6 0 27 5 24 0 2
|
||||||
|
0 6 5 3 5 5 44 6 24 1 -2
|
||||||
|
170 2 2 3 5 0 54 7 24 0 2
|
||||||
|
0 7 4 2 6 4 33 5 24 1 0
|
||||||
|
9 0 4 4 6 3 48 7 24 0 2
|
||||||
|
23 3 5 2 6 5 54 5 24 1 -2
|
||||||
|
0 0 6 2 5 6 56 3 24 1 -3
|
||||||
|
9 1 2 4 7 2 34 7 24 0 3
|
||||||
|
290 7 6 4 7 6 41 6 24 0 -1
|
||||||
|
1 0 5 1 5 6 40 6 24 1 -4
|
||||||
|
350 1 7 2 6 6 55 6 24 1 -4
|
||||||
|
20 0 4 3 5 5 38 6 24 1 0
|
||||||
|
0 3 6 2 6 6 40 6 24 1 -4
|
||||||
|
23 1 6 1 6 6 46 4 24 1 -5
|
||||||
|
150 4 3 3 4 0 26 6 24 0 1
|
||||||
|
31 0 5 2 7 3 49 6 24 0 -1
|
||||||
|
0 7 4 1 5 6 51 5 24 1 -2
|
||||||
|
9 2 5 2 6 4 46 6 24 0 -2
|
||||||
|
47 0 3 4 6 2 40 7 24 0 2
|
||||||
|
900 0 3 4 7 2 30 5 24 0 3
|
||||||
|
83 3 2 3 6 2 45 5 24 0 3
|
||||||
|
18 7 5 4 6 4 52 7 24 1 0
|
||||||
|
0 0 6 1 5 6 36 6 24 1 -4
|
||||||
|
20 0 4 3 5 3 49 6 24 0 0
|
||||||
|
24 7 3 4 5 1 38 7 24 0 1
|
||||||
|
18 0 2 4 6 1 51 7 24 0 2
|
||||||
|
9 3 3 2 5 1 47 6 24 0 1
|
||||||
|
0 1 6 1 5 6 52 7 24 1 -4
|
||||||
|
9 0 6 2 6 6 33 6 24 1 -4
|
||||||
|
0 4 4 2 6 6 50 4 24 1 0
|
||||||
|
18 7 6 2 5 4 48 7 24 1 -3
|
||||||
|
19 3 2 2 6 0 36 6 24 0 4
|
||||||
|
31 3 2 3 6 1 35 7 24 0 3
|
||||||
|
3500 7 7 3 5 4 34 7 24 0 -2
|
||||||
|
0 7 2 4 5 2 53 6 24 0 1
|
||||||
|
33 0 4 3 6 2 33 7 24 0 1
|
||||||
|
0 1 6 3 6 6 52 6 24 1 -3
|
||||||
|
18 3 4 3 6 4 44 7 24 0 1
|
||||||
|
0 0 3 4 4 0 48 6 24 0 0
|
||||||
|
31 3 5 2 6 5 20 4 24 1 -2
|
||||||
|
0 5 3 2 4 6 45 6 24 1 0
|
||||||
|
59 7 4 2 6 2 70 3 24 0 0
|
||||||
|
0 0 3 3 4 2 39 3 24 0 1
|
||||||
|
7300 7 3 3 5 1 40 7 24 1 2
|
||||||
|
75 4 5 2 7 5 62 6 24 1 -1
|
||||||
|
0 7 5 2 6 4 46 6 24 1 -2
|
||||||
|
27 7 4 4 7 2 46 3 24 0 3
|
||||||
|
1600 7 4 2 5 6 56 7 24 1 -1
|
||||||
|
0 7 6 3 6 6 55 7 24 1 -3
|
||||||
|
0 7 6 2 6 6 41 4 24 1 -4
|
||||||
|
7300 1 2 3 6 0 43 7 24 0 3
|
||||||
|
16 7 7 1 7 6 34 3 24 1 -6
|
||||||
|
0 7 7 1 6 4 73 6 24 1 -5
|
||||||
|
0 7 5 2 6 6 50 6 24 1 -2
|
||||||
|
0 3 6 2 7 5 43 6 24 1 -3
|
||||||
|
0 6 6 2 5 6 46 7 24 1 -3
|
||||||
|
18 7 4 2 6 3 61 7 24 1 0
|
||||||
|
@ -0,0 +1,13 @@
|
|||||||
|
d <- read.csv('./ccard.csv')
|
||||||
|
attach(d)
|
||||||
|
|
||||||
|
|
||||||
|
m1 <- lm(AVGEXP ~ AGE + INCOME + INCOMESQ + OWNRENT, weights=1/INCOMESQ)
|
||||||
|
results <- summary(m1)
|
||||||
|
|
||||||
|
m2 <- lm(AVGEXP ~ AGE + INCOME + INCOMESQ + OWNRENT - 1, weights=1/INCOMESQ)
|
||||||
|
results2 <- summary(m2)
|
||||||
|
|
||||||
|
print('m1 has a constant, which theoretically should be INCOME')
|
||||||
|
print('m2 include -1 for no constant')
|
||||||
|
print('See ccard/R_wls.s')
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,73 @@
|
|||||||
|
"AVGEXP","AGE","INCOME","INCOMESQ","OWNRENT"
|
||||||
|
124.98,38,4.52,20.4304,1
|
||||||
|
9.85,33,2.42,5.8564,0
|
||||||
|
15,34,4.5,20.25,1
|
||||||
|
137.87,31,2.54,6.4516,0
|
||||||
|
546.5,32,9.79,95.8441,1
|
||||||
|
92,23,2.5,6.25,0
|
||||||
|
40.83,28,3.96,15.6816,0
|
||||||
|
150.79,29,2.37,5.6169,1
|
||||||
|
777.82,37,3.8,14.44,1
|
||||||
|
52.58,28,3.2,10.24,0
|
||||||
|
256.66,31,3.95,15.6025,1
|
||||||
|
78.87,29,2.45,6.0025,1
|
||||||
|
42.62,35,1.91,3.6481,1
|
||||||
|
335.43,41,3.2,10.24,1
|
||||||
|
248.72,40,4,16,1
|
||||||
|
548.03,40,10,100,1
|
||||||
|
43.34,35,2.35,5.5225,1
|
||||||
|
218.52,34,2,4,1
|
||||||
|
170.64,36,4,16,0
|
||||||
|
37.58,43,5.14,26.4196,1
|
||||||
|
502.2,30,4.51,20.3401,0
|
||||||
|
73.18,22,1.5,2.25,0
|
||||||
|
1532.77,40,5.5,30.25,1
|
||||||
|
42.69,22,2.03,4.1209,0
|
||||||
|
417.83,29,3.2,10.24,0
|
||||||
|
552.72,21,2.47,6.1009,1
|
||||||
|
222.54,24,3,9,0
|
||||||
|
541.3,43,3.54,12.5316,1
|
||||||
|
568.77,37,5.7,32.49,1
|
||||||
|
344.47,27,3.5,12.25,0
|
||||||
|
405.35,28,4.6,21.16,1
|
||||||
|
310.94,26,3,9,1
|
||||||
|
53.65,23,2.59,6.7081,0
|
||||||
|
63.92,30,1.51,2.2801,0
|
||||||
|
165.85,30,1.85,3.4225,0
|
||||||
|
9.58,38,2.6,6.76,0
|
||||||
|
319.49,36,2,4,0
|
||||||
|
83.08,26,2.35,5.5225,0
|
||||||
|
644.83,28,7,49,1
|
||||||
|
93.2,24,2,4,0
|
||||||
|
105.04,21,1.7,2.89,0
|
||||||
|
34.13,24,2.8,7.84,0
|
||||||
|
41.19,26,2.4,5.76,0
|
||||||
|
169.89,33,3,9,0
|
||||||
|
1898.03,34,4.8,23.04,0
|
||||||
|
810.39,33,3.18,10.1124,0
|
||||||
|
32.78,21,1.5,2.25,0
|
||||||
|
95.8,25,3,9,0
|
||||||
|
27.78,27,2.28,5.1984,0
|
||||||
|
215.07,26,2.8,7.84,0
|
||||||
|
79.51,22,2.7,7.29,0
|
||||||
|
306.03,41,6,36,0
|
||||||
|
104.54,42,3.9,15.21,0
|
||||||
|
642.47,25,3.07,9.4249,0
|
||||||
|
308.05,31,2.46,6.0516,1
|
||||||
|
186.35,27,2,4,0
|
||||||
|
56.15,33,3.25,10.5625,0
|
||||||
|
129.37,37,2.72,7.3984,0
|
||||||
|
93.11,27,2.2,4.84,0
|
||||||
|
292.66,24,3.75,14.0625,0
|
||||||
|
98.46,25,2.88,8.2944,0
|
||||||
|
258.55,36,3.05,9.3025,0
|
||||||
|
101.68,33,2.55,6.5025,0
|
||||||
|
65.25,55,2.64,6.9696,1
|
||||||
|
108.61,20,1.65,2.7225,0
|
||||||
|
49.56,29,2.4,5.76,0
|
||||||
|
235.57,41,7.24,52.4176,1
|
||||||
|
68.38,43,2.4,5.76,0
|
||||||
|
474.15,33,6,36,1
|
||||||
|
234.05,25,3.6,12.96,0
|
||||||
|
451.2,26,5,25,1
|
||||||
|
251.52,46,5.5,30.25,1
|
||||||
|
@ -0,0 +1,57 @@
|
|||||||
|
"""Bill Greene's credit scoring data."""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """Used with express permission of the original author, who
|
||||||
|
retains all rights."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
William Greene's `Econometric Analysis`
|
||||||
|
|
||||||
|
More information can be found at the web site of the text:
|
||||||
|
http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """William Greene's credit scoring data"""
|
||||||
|
|
||||||
|
DESCRLONG = """More information on this data can be found on the
|
||||||
|
homepage for Greene's `Econometric Analysis`. See source.
|
||||||
|
"""
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of observations - 72
|
||||||
|
Number of variables - 5
|
||||||
|
Variable name definitions - See Source for more information on the variables.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""Load the credit card data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""Load the credit card data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=0)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/ccard.csv', 'rb'), delimiter=",",
|
||||||
|
names=True, dtype=float)
|
||||||
|
return data
|
||||||
@ -0,0 +1,101 @@
|
|||||||
|
"MDR","Acc","Age","Income","Avgexp","Ownrent","Selfempl"
|
||||||
|
0,1,38,4.52,124.98,1,0
|
||||||
|
0,1,33,2.42,9.85,0,0
|
||||||
|
0,1,34,4.5,15,1,0
|
||||||
|
0,1,31,2.54,137.87,0,0
|
||||||
|
0,1,32,9.79,546.5,1,0
|
||||||
|
0,1,23,2.5,92,0,0
|
||||||
|
0,1,28,3.96,40.83,0,0
|
||||||
|
0,1,29,2.37,150.79,1,0
|
||||||
|
0,1,37,3.8,777.82,1,0
|
||||||
|
0,1,28,3.2,52.58,0,0
|
||||||
|
0,1,31,3.95,256.66,1,0
|
||||||
|
0,0,42,1.98,0,1,0
|
||||||
|
0,0,30,1.73,0,1,0
|
||||||
|
0,1,29,2.45,78.87,1,0
|
||||||
|
0,1,35,1.91,42.62,1,0
|
||||||
|
0,1,41,3.2,335.43,1,0
|
||||||
|
0,1,40,4,248.72,1,0
|
||||||
|
7,0,30,3,0,1,0
|
||||||
|
0,1,40,10,548.03,1,1
|
||||||
|
3,0,46,3.4,0,0,0
|
||||||
|
0,1,35,2.35,43.34,1,0
|
||||||
|
1,0,25,1.88,0,0,0
|
||||||
|
0,1,34,2,218.52,1,0
|
||||||
|
1,1,36,4,170.64,0,0
|
||||||
|
0,1,43,5.14,37.58,1,0
|
||||||
|
0,1,30,4.51,502.2,0,0
|
||||||
|
0,0,22,3.84,0,0,1
|
||||||
|
0,1,22,1.5,73.18,0,0
|
||||||
|
0,0,34,2.5,0,1,0
|
||||||
|
0,1,40,5.5,1532.77,1,0
|
||||||
|
0,1,22,2.03,42.69,0,0
|
||||||
|
1,1,29,3.2,417.83,0,0
|
||||||
|
1,0,25,3.15,0,1,0
|
||||||
|
0,1,21,2.47,552.72,1,0
|
||||||
|
0,1,24,3,222.54,0,0
|
||||||
|
0,1,43,3.54,541.3,1,0
|
||||||
|
0,0,43,2.28,0,0,0
|
||||||
|
0,1,37,5.7,568.77,1,0
|
||||||
|
0,1,27,3.5,344.47,0,0
|
||||||
|
0,1,28,4.6,405.35,1,0
|
||||||
|
0,1,26,3,310.94,1,0
|
||||||
|
0,1,23,2.59,53.65,0,0
|
||||||
|
0,1,30,1.51,63.92,0,0
|
||||||
|
0,1,30,1.85,165.85,0,0
|
||||||
|
0,1,38,2.6,9.58,0,0
|
||||||
|
0,0,28,1.8,0,0,1
|
||||||
|
0,1,36,2,319.49,0,0
|
||||||
|
0,0,38,3.26,0,0,0
|
||||||
|
0,1,26,2.35,83.08,0,0
|
||||||
|
0,1,28,7,644.83,1,0
|
||||||
|
0,0,50,3.6,0,0,0
|
||||||
|
0,1,24,2,93.2,0,0
|
||||||
|
0,1,21,1.7,105.04,0,0
|
||||||
|
0,1,24,2.8,34.13,0,0
|
||||||
|
0,1,26,2.4,41.19,0,0
|
||||||
|
1,1,33,3,169.89,0,0
|
||||||
|
0,1,34,4.8,1898.03,0,0
|
||||||
|
0,1,33,3.18,810.39,0,0
|
||||||
|
0,0,45,1.8,0,0,0
|
||||||
|
0,1,21,1.5,32.78,0,0
|
||||||
|
2,1,25,3,95.8,0,0
|
||||||
|
0,1,27,2.28,27.78,0,0
|
||||||
|
0,1,26,2.8,215.07,0,0
|
||||||
|
0,1,22,2.7,79.51,0,0
|
||||||
|
3,0,27,4.9,0,1,0
|
||||||
|
0,0,26,2.5,0,0,1
|
||||||
|
0,1,41,6,306.03,0,1
|
||||||
|
0,1,42,3.9,104.54,0,0
|
||||||
|
0,0,22,5.1,0,0,0
|
||||||
|
0,1,25,3.07,642.47,0,0
|
||||||
|
0,1,31,2.46,308.05,1,0
|
||||||
|
0,1,27,2,186.35,0,0
|
||||||
|
0,1,33,3.25,56.15,0,0
|
||||||
|
0,1,37,2.72,129.37,0,0
|
||||||
|
0,1,27,2.2,93.11,0,0
|
||||||
|
1,0,24,4.1,0,0,0
|
||||||
|
0,1,24,3.75,292.66,0,0
|
||||||
|
0,1,25,2.88,98.46,0,0
|
||||||
|
0,1,36,3.05,258.55,0,0
|
||||||
|
0,1,33,2.55,101.68,0,0
|
||||||
|
0,0,33,4,0,0,0
|
||||||
|
1,1,55,2.64,65.25,1,0
|
||||||
|
0,1,20,1.65,108.61,0,0
|
||||||
|
0,1,29,2.4,49.56,0,0
|
||||||
|
3,0,40,3.71,0,0,0
|
||||||
|
0,1,41,7.24,235.57,1,0
|
||||||
|
0,0,41,4.39,0,1,0
|
||||||
|
0,0,35,3.3,0,1,0
|
||||||
|
0,0,24,2.3,0,0,0
|
||||||
|
1,0,54,4.18,0,0,0
|
||||||
|
2,0,34,2.49,0,0,0
|
||||||
|
0,0,45,2.81,0,1,0
|
||||||
|
0,1,43,2.4,68.38,0,0
|
||||||
|
4,0,35,1.5,0,0,0
|
||||||
|
2,0,36,8.4,0,0,0
|
||||||
|
0,1,22,1.56,0,0,0
|
||||||
|
1,1,33,6,474.15,1,0
|
||||||
|
1,1,25,3.6,234.05,0,0
|
||||||
|
0,1,26,5,451.2,1,0
|
||||||
|
0,1,46,5.5,251.52,1,0
|
||||||
|
@ -0,0 +1,14 @@
|
|||||||
|
MDR = Number of derogator reports
|
||||||
|
|
||||||
|
Acc = Credit card application accpeted (1=yes)
|
||||||
|
|
||||||
|
Age = Age in years + 12ths of a year
|
||||||
|
|
||||||
|
Income = Income divided by 10,000
|
||||||
|
|
||||||
|
Avgexp = Avg. monthly credit card expenditure
|
||||||
|
|
||||||
|
Ownrent = Indiviual owns(1) or rents(0) home
|
||||||
|
|
||||||
|
Selfempl = (1=yes, 0=no)
|
||||||
|
|
||||||
@ -0,0 +1,11 @@
|
|||||||
|
### SETUP ###
|
||||||
|
d <- read.table("./committee.csv",sep=",", header=T)
|
||||||
|
attach(d)
|
||||||
|
|
||||||
|
LNSTAFF <- log(STAFF)
|
||||||
|
SUBS.LNSTAFF <- SUBS*LNSTAFF
|
||||||
|
library(MASS)
|
||||||
|
#m1 <- glm.nb(BILLS104 ~ SIZE + SUBS + LNSTAFF + PRESTIGE + BILLS103 + SUBS.LNSTAFF)
|
||||||
|
m1 <- glm(BILLS104 ~ SIZE + SUBS + LNSTAFF + PRESTIGE + BILLS103 + SUBS.LNSTAFF, family=negative.binomial(1)) # Disp should be 1 by default
|
||||||
|
|
||||||
|
results <- summary.glm(m1)
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
"COMMITTEE","BILLS104","SIZE","SUBS","STAFF","PRESTIGE","BILLS103"
|
||||||
|
"Appropriations",6,58,13,109,1,9
|
||||||
|
"Budget",23,42,0,39,1,101
|
||||||
|
"Rules",44,13,2,25,1,54
|
||||||
|
"Ways_and_Means",355,39,5,23,1,542
|
||||||
|
"Banking",125,51,5,61,0,101
|
||||||
|
"Economic_Educ_Oppor",131,43,5,69,0,158
|
||||||
|
"Commerce",271,49,4,79,0,196
|
||||||
|
"International_Relations",63,44,3,68,0,40
|
||||||
|
"Government_Reform",149,51,7,99,0,72
|
||||||
|
"Judiciary",253,35,5,56,0,168
|
||||||
|
"Agriculture",81,49,5,46,0,60
|
||||||
|
"National_Security",89,55,7,48,0,75
|
||||||
|
"Resources",142,44,5,58,0,98
|
||||||
|
"TransInfrastructure",155,61,6,74,0,69
|
||||||
|
"Science",27,50,4,58,0,25
|
||||||
|
"Small_Business",8,43,4,29,0,9
|
||||||
|
"Veterans_Affairs",28,33,3,36,0,41
|
||||||
|
"House_Oversight",68,12,0,24,0,233
|
||||||
|
"Stds_of_Conduct",1,10,0,9,0,0
|
||||||
|
"Intelligence",4,16,2,24,0,2
|
||||||
|
@ -0,0 +1,71 @@
|
|||||||
|
"""First 100 days of the US House of Representatives 1995"""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """Used with express permission from the original author,
|
||||||
|
who retains all rights."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
Jeff Gill's `Generalized Linear Models: A Unifited Approach`
|
||||||
|
|
||||||
|
http://jgill.wustl.edu/research/books.html
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """Number of bill assignments in the 104th House in 1995"""
|
||||||
|
|
||||||
|
DESCRLONG = """The example in Gill, seeks to explain the number of bill
|
||||||
|
assignments in the first 100 days of the US' 104th House of Representatives.
|
||||||
|
The response variable is the number of bill assignments in the first 100 days
|
||||||
|
over 20 Committees. The explanatory variables in the example are the number of
|
||||||
|
assignments in the first 100 days of the 103rd House, the number of members on
|
||||||
|
the committee, the number of subcommittees, the log of the number of staff
|
||||||
|
assigned to the committee, a dummy variable indicating whether
|
||||||
|
the committee is a high prestige committee, and an interaction term between
|
||||||
|
the number of subcommittees and the log of the staff size.
|
||||||
|
|
||||||
|
The data returned by load are not cleaned to represent the above example.
|
||||||
|
"""
|
||||||
|
|
||||||
|
NOTE = """Number of Observations - 20
|
||||||
|
|
||||||
|
Number of Variables - 6
|
||||||
|
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
BILLS104 - Number of bill assignments in the first 100 days of the 104th
|
||||||
|
House of Representatives.
|
||||||
|
SIZE - Number of members on the committee.
|
||||||
|
SUBS - Number of subcommittees.
|
||||||
|
STAFF - Number of staff members assigned to the committee.
|
||||||
|
PRESTIGE - PRESTIGE == 1 is a high prestige committee.
|
||||||
|
BILLS103 - Number of bill assignments in the first 100 days of the 103rd
|
||||||
|
House of Representatives.
|
||||||
|
|
||||||
|
Committee names are included as a variable in the data file though not
|
||||||
|
returned by load.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""Load the committee data and returns a data class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
--------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/committee.csv', 'rb'), delimiter=",",
|
||||||
|
names=True, dtype=float, usecols=(1,2,3,4,5,6))
|
||||||
|
return data
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
SIZE SUBS STAFF PRESTIGE POLICY CONSTIT SERVICE BILLS103 BILLS104
|
||||||
|
Appropriations 58 13 109 1 0 0 0 9 6
|
||||||
|
Budget 42 0 39 1 0 0 0 101 23
|
||||||
|
Rules 13 2 25 1 0 0 0 54 44
|
||||||
|
Ways_and_Means 39 5 23 1 0 0 0 542 355
|
||||||
|
Banking 51 5 61 0 1 0 0 101 125
|
||||||
|
Economic_Educ_Oppor 43 5 69 0 1 0 0 158 131
|
||||||
|
Commerce 49 4 79 0 1 0 0 196 271
|
||||||
|
International_Relations 44 3 68 0 1 0 0 40 63
|
||||||
|
Government_Reform 51 7 99 0 1 0 0 72 149
|
||||||
|
Judiciary 35 5 56 0 1 0 0 168 253
|
||||||
|
Agriculture 49 5 46 0 0 1 0 60 81
|
||||||
|
National_Security 55 7 48 0 0 1 0 75 89
|
||||||
|
Resources 44 5 58 0 0 1 0 98 142
|
||||||
|
TransInfrastructure 61 6 74 0 0 1 0 69 155
|
||||||
|
Science 50 4 58 0 0 1 0 25 27
|
||||||
|
Small_Business 43 4 29 0 0 1 0 9 8
|
||||||
|
Veterans_Affairs 33 3 36 0 0 1 0 41 28
|
||||||
|
House_Oversight 12 0 24 0 0 0 1 233 68
|
||||||
|
Stds_of_Conduct 10 0 9 0 0 0 1 0 1
|
||||||
|
Intelligence 16 2 24 0 0 0 1 2 4
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,26 @@
|
|||||||
|
"YEAR","WORLDCONSUMPTION","COPPERPRICE","INCOMEINDEX","ALUMPRICE","INVENTORYINDEX","TIME"
|
||||||
|
1951,3173,26.56,0.7,19.76,0.98,1
|
||||||
|
1952,3281.1,27.31,0.71,20.78,1.04,2
|
||||||
|
1953,3135.7,32.95,0.72,22.55,1.05,3
|
||||||
|
1954,3359.1,33.9,0.7,23.06,0.97,4
|
||||||
|
1955,3755.1,42.7,0.74,24.93,1.02,5
|
||||||
|
1956,3875.9,46.11,0.74,26.5,1.04,6
|
||||||
|
1957,3905.7,31.7,0.74,27.24,0.98,7
|
||||||
|
1958,3957.6,27.23,0.72,26.21,0.98,8
|
||||||
|
1959,4279.1,32.89,0.75,26.09,1.03,9
|
||||||
|
1960,4627.9,33.78,0.77,27.4,1.03,10
|
||||||
|
1961,4910.2,31.66,0.76,26.94,0.98,11
|
||||||
|
1962,4908.4,32.28,0.79,25.18,1,12
|
||||||
|
1963,5327.9,32.38,0.83,23.94,0.97,13
|
||||||
|
1964,5878.4,33.75,0.85,25.07,1.03,14
|
||||||
|
1965,6075.2,36.25,0.89,25.37,1.08,15
|
||||||
|
1966,6312.7,36.24,0.93,24.55,1.05,16
|
||||||
|
1967,6056.8,38.23,0.95,24.98,1.03,17
|
||||||
|
1968,6375.9,40.83,0.99,24.96,1.03,18
|
||||||
|
1969,6974.3,44.62,1,25.52,0.99,19
|
||||||
|
1970,7101.6,52.27,1,26.01,1,20
|
||||||
|
1971,7071.7,45.16,1.02,25.46,0.96,21
|
||||||
|
1972,7754.8,42.5,1.07,22.17,0.97,22
|
||||||
|
1973,8480.3,43.7,1.12,18.56,0.98,23
|
||||||
|
1974,8105.2,47.88,1.1,21.32,1.01,24
|
||||||
|
1975,7157.2,36.33,1.07,22.75,0.94,25
|
||||||
|
@ -0,0 +1,74 @@
|
|||||||
|
"""World Copper Prices 1951-1975 dataset."""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """Used with express permission from the original author,
|
||||||
|
who retains all rights."""
|
||||||
|
TITLE = "World Copper Market 1951-1975 Dataset"
|
||||||
|
SOURCE = """
|
||||||
|
Jeff Gill's `Generalized Linear Models: A Unified Approach`
|
||||||
|
|
||||||
|
http://jgill.wustl.edu/research/books.html
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """World Copper Market 1951-1975"""
|
||||||
|
|
||||||
|
DESCRLONG = """This data describes the world copper market from 1951 through 1975. In an
|
||||||
|
example, in Gill, the outcome variable (of a 2 stage estimation) is the world
|
||||||
|
consumption of copper for the 25 years. The explanatory variables are the
|
||||||
|
world consumption of copper in 1000 metric tons, the constant dollar adjusted
|
||||||
|
price of copper, the price of a substitute, aluminum, an index of real per
|
||||||
|
capita income base 1970, an annual measure of manufacturer inventory change,
|
||||||
|
and a time trend.
|
||||||
|
"""
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of Observations - 25
|
||||||
|
|
||||||
|
Number of Variables - 6
|
||||||
|
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
WORLDCONSUMPTION - World consumption of copper (in 1000 metric tons)
|
||||||
|
COPPERPRICE - Constant dollar adjusted price of copper
|
||||||
|
INCOMEINDEX - An index of real per capita income (base 1970)
|
||||||
|
ALUMPRICE - The price of aluminum
|
||||||
|
INVENTORYINDEX - A measure of annual manufacturer inventory trend
|
||||||
|
TIME - A time trend
|
||||||
|
|
||||||
|
Years are included in the data file though not returned by load.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Load the copper data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
--------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/copper.csv', 'rb'), delimiter=",",
|
||||||
|
names=True, dtype=float, usecols=(1,2,3,4,5,6))
|
||||||
|
return data
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""
|
||||||
|
Load the copper data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
--------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=0, dtype=float)
|
||||||
@ -0,0 +1,26 @@
|
|||||||
|
YEAR WORLDCONSUMPTION COPPERPRICE INCOMEINDEX ALUMPRICE INVENTORYINDEX TIME
|
||||||
|
1951 3173.0 26.56 0.70 19.76 0.97679 1
|
||||||
|
1952 3281.1 27.31 0.71 20.78 1.03937 2
|
||||||
|
1953 3135.7 32.95 0.72 22.55 1.05153 3
|
||||||
|
1954 3359.1 33.90 0.70 23.06 0.97312 4
|
||||||
|
1955 3755.1 42.70 0.74 24.93 1.02349 5
|
||||||
|
1956 3875.9 46.11 0.74 26.50 1.04135 6
|
||||||
|
1957 3905.7 31.70 0.74 27.24 0.97686 7
|
||||||
|
1958 3957.6 27.23 0.72 26.21 0.98069 8
|
||||||
|
1959 4279.1 32.89 0.75 26.09 1.02888 9
|
||||||
|
1960 4627.9 33.78 0.77 27.40 1.03392 10
|
||||||
|
1961 4910.2 31.66 0.76 26.94 0.97922 11
|
||||||
|
1962 4908.4 32.28 0.79 25.18 0.99679 12
|
||||||
|
1963 5327.9 32.38 0.83 23.94 0.96630 13
|
||||||
|
1964 5878.4 33.75 0.85 25.07 1.02915 14
|
||||||
|
1965 6075.2 36.25 0.89 25.37 1.07950 15
|
||||||
|
1966 6312.7 36.24 0.93 24.55 1.05073 16
|
||||||
|
1967 6056.8 38.23 0.95 24.98 1.02788 17
|
||||||
|
1968 6375.9 40.83 0.99 24.96 1.02799 18
|
||||||
|
1969 6974.3 44.62 1.00 25.52 0.99151 19
|
||||||
|
1970 7101.6 52.27 1.00 26.01 1.00191 20
|
||||||
|
1971 7071.7 45.16 1.02 25.46 0.95644 21
|
||||||
|
1972 7754.8 42.50 1.07 22.17 0.96947 22
|
||||||
|
1973 8480.3 43.70 1.12 18.56 0.98220 23
|
||||||
|
1974 8105.2 47.88 1.10 21.32 1.00793 24
|
||||||
|
1975 7157.2 36.33 1.07 22.75 0.93810 25
|
||||||
@ -0,0 +1,11 @@
|
|||||||
|
### SETUP ###
|
||||||
|
d <- read.table("./cpunish.csv",sep=",", header=T)
|
||||||
|
attach(d)
|
||||||
|
LN_VC100k96 = log(VC100k96)
|
||||||
|
### MODEL ###
|
||||||
|
m1 <- glm(EXECUTIONS ~ INCOME + PERPOVERTY + PERBLACK + LN_VC100k96 + SOUTH + DEGREE,
|
||||||
|
family=poisson)
|
||||||
|
results <- summary.glm(m1)
|
||||||
|
results
|
||||||
|
results['coefficients']
|
||||||
|
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,18 @@
|
|||||||
|
"STATE","EXECUTIONS","INCOME","PERPOVERTY","PERBLACK","VC100k96","SOUTH","DEGREE"
|
||||||
|
"Texas",37,34453,16.7,12.2,644,1,0.16
|
||||||
|
"Virginia",9,41534,12.5,20,351,1,0.27
|
||||||
|
"Missouri",6,35802,10.6,11.2,591,0,0.21
|
||||||
|
"Arkansas",4,26954,18.4,16.1,524,1,0.16
|
||||||
|
"Alabama",3,31468,14.8,25.9,565,1,0.19
|
||||||
|
"Arizona",2,32552,18.8,3.5,632,0,0.25
|
||||||
|
"Illinois",2,40873,11.6,15.3,886,0,0.25
|
||||||
|
"South_Carolina",2,34861,13.1,30.1,997,1,0.21
|
||||||
|
"Colorado",1,42562,9.4,4.3,405,0,0.31
|
||||||
|
"Florida",1,31900,14.3,15.4,1051,1,0.24
|
||||||
|
"Indiana",1,37421,8.2,8.2,537,0,0.19
|
||||||
|
"Kentucky",1,33305,16.4,7.2,321,0,0.16
|
||||||
|
"Louisiana",1,32108,18.4,32.1,929,1,0.18
|
||||||
|
"Maryland",1,45844,9.3,27.4,931,0,0.29
|
||||||
|
"Nebraska",1,34743,10,4,435,0,0.24
|
||||||
|
"Oklahoma",1,29709,15.2,7.7,597,0,0.21
|
||||||
|
"Oregon",1,36777,11.7,1.8,463,0,0.25
|
||||||
|
@ -0,0 +1,78 @@
|
|||||||
|
"""US Capital Punishment dataset."""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """Used with express permission from the original author,
|
||||||
|
who retains all rights."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
Jeff Gill's `Generalized Linear Models: A Unified Approach`
|
||||||
|
|
||||||
|
http://jgill.wustl.edu/research/books.html
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """Number of state executions in 1997"""
|
||||||
|
|
||||||
|
DESCRLONG = """This data describes the number of times capital punishment is implemented
|
||||||
|
at the state level for the year 1997. The outcome variable is the number of
|
||||||
|
executions. There were executions in 17 states.
|
||||||
|
Included in the data are explanatory variables for median per capita income
|
||||||
|
in dollars, the percent of the population classified as living in poverty,
|
||||||
|
the percent of Black citizens in the population, the rate of violent
|
||||||
|
crimes per 100,000 residents for 1996, a dummy variable indicating
|
||||||
|
whether the state is in the South, and (an estimate of) the proportion
|
||||||
|
of the population with a college degree of some kind.
|
||||||
|
"""
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of Observations - 17
|
||||||
|
|
||||||
|
Number of Variables - 7
|
||||||
|
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
EXECUTIONS - Executions in 1996
|
||||||
|
INCOME - Median per capita income in 1996 dollars
|
||||||
|
PERPOVERTY - Percent of the population classified as living in poverty
|
||||||
|
PERBLACK - Percent of black citizens in the population
|
||||||
|
VC100k96 - Rate of violent crimes per 100,00 residents for 1996
|
||||||
|
SOUTH - SOUTH == 1 indicates a state in the South
|
||||||
|
DEGREE - An esimate of the proportion of the state population with a
|
||||||
|
college degree of some kind
|
||||||
|
|
||||||
|
State names are included in the data file, though not returned by load.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Load the cpunish data and return a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""
|
||||||
|
Load the cpunish data and return a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/cpunish.csv', 'rb'), delimiter=",",
|
||||||
|
names=True, dtype=float, usecols=(1,2,3,4,5,6,7))
|
||||||
|
return data
|
||||||
@ -0,0 +1,18 @@
|
|||||||
|
STATE EXECUTIONS INCOME PERPOVERTY PERBLACK VC100k96 SOUTH <9thGRADE 9thTO12th HSOREQUIV SOMECOLL AADEGREE BACHELORS GRAD/PROF
|
||||||
|
Texas 37 34453 16.7 12.2 644 1 1492112 1924831 3153187 2777973 598956 530849 673250
|
||||||
|
Virginia 9 41534 12.5 20.0 351 1 461475 669851 1297714 969191 244488 676710 363602
|
||||||
|
Missouri 6 35802 10.6 11.2 591 0 391097 578440 1251550 785555 170146 420521 204294
|
||||||
|
Arkansas 4 26954 18.4 16.1 524 1 234071 328690 571252 323016 62246 143038 67144
|
||||||
|
Alabama 3 31468 14.8 25.9 565 1 362434 597455 875703 575123 146228 281466 142177
|
||||||
|
Arizona 2 32552 18.8 3.5 632 0 224662 368279 708340 724228 173801 325575 161560
|
||||||
|
Illinois 2 40873 11.6 15.3 886 0 786815 1203134 2531465 1817238 490791 1101193 552145
|
||||||
|
South_Carolina 2 34861 13.1 30.1 997 1 303694 479916 776053 466145 152671 267365 118811
|
||||||
|
Colorado 1 42562 9.4 4.3 405 0 124477 270560 654510 630445 161331 402917 190168
|
||||||
|
Florida 1 31900 14.3 15.4 1051 1 883820 1706839 3045682 2054574 682005 1133053 567453
|
||||||
|
Indiana 1 37421 8.2 8.2 537 0 310403 673362 1530741 775605 212379 360087 224057
|
||||||
|
Kentucky 1 33305 16.4 7.2 321 0 456107 467956 881795 476362 108409 209055 129994
|
||||||
|
Louisiana 1 32108 18.4 32.1 929 1 391630 534570 951832 586477 94409 288154 143624
|
||||||
|
Maryland 1 45844 9.3 27.4 931 0 257518 514788 1044976 744604 182465 532883 342012
|
||||||
|
Nebraska 1 34743 10.0 4.0 435 0 81690 124792 388540 272981 80956 141231 59008
|
||||||
|
Oklahoma 1 29709 15.2 7.7 597 0 201228 375155 706003 539511 113434 253635 119774
|
||||||
|
Oregon 1 36777 11.7 1.8 463 0 122513 283409 613983 561176 139269 267161 130403
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,90 @@
|
|||||||
|
"""Grunfeld (1950) Investment Data"""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """This is public domain."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """This is the Grunfeld (1950) Investment Data.
|
||||||
|
|
||||||
|
The source for the data was the original 11-firm data set from Grunfeld's Ph.D.
|
||||||
|
thesis recreated by Kleiber and Zeileis (2008) "The Grunfeld Data at 50".
|
||||||
|
The data can be found here.
|
||||||
|
http://statmath.wu-wien.ac.at/~zeileis/grunfeld/
|
||||||
|
|
||||||
|
For a note on the many versions of the Grunfeld data circulating see:
|
||||||
|
http://www.stanford.edu/~clint/bench/grunfeld.htm
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """Grunfeld (1950) Investment Data for 11 U.S. Firms."""
|
||||||
|
|
||||||
|
DESCRLONG = DESCRSHORT
|
||||||
|
|
||||||
|
NOTE = """Number of observations - 220 (20 years for 11 firms)
|
||||||
|
|
||||||
|
Number of variables - 5
|
||||||
|
|
||||||
|
Variables name definitions::
|
||||||
|
|
||||||
|
invest - Gross investment in 1947 dollars
|
||||||
|
value - Market value as of Dec. 31 in 1947 dollars
|
||||||
|
capital - Stock of plant and equipment in 1947 dollars
|
||||||
|
firm - General Motors, US Steel, General Electric, Chrysler,
|
||||||
|
Atlantic Refining, IBM, Union Oil, Westinghouse, Goodyear,
|
||||||
|
Diamond Match, American Steel
|
||||||
|
year - 1935 - 1954
|
||||||
|
|
||||||
|
Note that raw_data has firm expanded to dummy variables, since it is a
|
||||||
|
string categorical variable.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
from scikits.statsmodels.tools import categorical
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Loads the Grunfeld data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
|
||||||
|
Notes
|
||||||
|
-----
|
||||||
|
raw_data has the firm variable expanded to dummy variables for each
|
||||||
|
firm (ie., there is no reference dummy)
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
raw_data = categorical(data, col='firm', drop=True)
|
||||||
|
ds = du.process_recarray(data, endog_idx=0, stack=False)
|
||||||
|
ds.raw_data = raw_data
|
||||||
|
return ds
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""
|
||||||
|
Loads the Grunfeld data and returns a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
|
||||||
|
Notes
|
||||||
|
-----
|
||||||
|
raw_data has the firm variable expanded to dummy variables for each
|
||||||
|
firm (ie., there is no reference dummy)
|
||||||
|
"""
|
||||||
|
from pandas import DataFrame
|
||||||
|
data = _get_data()
|
||||||
|
raw_data = categorical(data, col='firm', drop=True)
|
||||||
|
ds = du.process_recarray_pandas(data, endog_idx=0)
|
||||||
|
ds.raw_data = DataFrame(raw_data)
|
||||||
|
return ds
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/grunfeld.csv','rb'), delimiter=",",
|
||||||
|
names=True, dtype="f8,f8,f8,a17,f8")
|
||||||
|
return data
|
||||||
@ -0,0 +1,221 @@
|
|||||||
|
invest,value,capital,firm,year
|
||||||
|
317.6,3078.5,2.8,General Motors,1935
|
||||||
|
391.8,4661.7,52.6,General Motors,1936
|
||||||
|
410.6,5387.1,156.9,General Motors,1937
|
||||||
|
257.7,2792.2,209.2,General Motors,1938
|
||||||
|
330.8,4313.2,203.4,General Motors,1939
|
||||||
|
461.2,4643.9,207.2,General Motors,1940
|
||||||
|
512,4551.2,255.2,General Motors,1941
|
||||||
|
448,3244.1,303.7,General Motors,1942
|
||||||
|
499.6,4053.7,264.1,General Motors,1943
|
||||||
|
547.5,4379.3,201.6,General Motors,1944
|
||||||
|
561.2,4840.9,265,General Motors,1945
|
||||||
|
688.1,4900.9,402.2,General Motors,1946
|
||||||
|
568.9,3526.5,761.5,General Motors,1947
|
||||||
|
529.2,3254.7,922.4,General Motors,1948
|
||||||
|
555.1,3700.2,1020.1,General Motors,1949
|
||||||
|
642.9,3755.6,1099,General Motors,1950
|
||||||
|
755.9,4833,1207.7,General Motors,1951
|
||||||
|
891.2,4924.9,1430.5,General Motors,1952
|
||||||
|
1304.4,6241.7,1777.3,General Motors,1953
|
||||||
|
1486.7,5593.6,2226.3,General Motors,1954
|
||||||
|
209.9,1362.4,53.8,US Steel,1935
|
||||||
|
355.3,1807.1,50.5,US Steel,1936
|
||||||
|
469.9,2676.3,118.1,US Steel,1937
|
||||||
|
262.3,1801.9,260.2,US Steel,1938
|
||||||
|
230.4,1957.3,312.7,US Steel,1939
|
||||||
|
361.6,2202.9,254.2,US Steel,1940
|
||||||
|
472.8,2380.5,261.4,US Steel,1941
|
||||||
|
445.6,2168.6,298.7,US Steel,1942
|
||||||
|
361.6,1985.1,301.8,US Steel,1943
|
||||||
|
288.2,1813.9,279.1,US Steel,1944
|
||||||
|
258.7,1850.2,213.8,US Steel,1945
|
||||||
|
420.3,2067.7,132.6,US Steel,1946
|
||||||
|
420.5,1796.7,264.8,US Steel,1947
|
||||||
|
494.5,1625.8,306.9,US Steel,1948
|
||||||
|
405.1,1667,351.1,US Steel,1949
|
||||||
|
418.8,1677.4,357.8,US Steel,1950
|
||||||
|
588.2,2289.5,342.1,US Steel,1951
|
||||||
|
645.5,2159.4,444.2,US Steel,1952
|
||||||
|
641,2031.3,623.6,US Steel,1953
|
||||||
|
459.3,2115.5,669.7,US Steel,1954
|
||||||
|
33.1,1170.6,97.8,General Electric,1935
|
||||||
|
45,2015.8,104.4,General Electric,1936
|
||||||
|
77.2,2803.3,118,General Electric,1937
|
||||||
|
44.6,2039.7,156.2,General Electric,1938
|
||||||
|
48.1,2256.2,172.6,General Electric,1939
|
||||||
|
74.4,2132.2,186.6,General Electric,1940
|
||||||
|
113,1834.1,220.9,General Electric,1941
|
||||||
|
91.9,1588,287.8,General Electric,1942
|
||||||
|
61.3,1749.4,319.9,General Electric,1943
|
||||||
|
56.8,1687.2,321.3,General Electric,1944
|
||||||
|
93.6,2007.7,319.6,General Electric,1945
|
||||||
|
159.9,2208.3,346,General Electric,1946
|
||||||
|
147.2,1656.7,456.4,General Electric,1947
|
||||||
|
146.3,1604.4,543.4,General Electric,1948
|
||||||
|
98.3,1431.8,618.3,General Electric,1949
|
||||||
|
93.5,1610.5,647.4,General Electric,1950
|
||||||
|
135.2,1819.4,671.3,General Electric,1951
|
||||||
|
157.3,2079.7,726.1,General Electric,1952
|
||||||
|
179.5,2371.6,800.3,General Electric,1953
|
||||||
|
189.6,2759.9,888.9,General Electric,1954
|
||||||
|
40.29,417.5,10.5,Chrysler,1935
|
||||||
|
72.76,837.8,10.2,Chrysler,1936
|
||||||
|
66.26,883.9,34.7,Chrysler,1937
|
||||||
|
51.6,437.9,51.8,Chrysler,1938
|
||||||
|
52.41,679.7,64.3,Chrysler,1939
|
||||||
|
69.41,727.8,67.1,Chrysler,1940
|
||||||
|
68.35,643.6,75.2,Chrysler,1941
|
||||||
|
46.8,410.9,71.4,Chrysler,1942
|
||||||
|
47.4,588.4,67.1,Chrysler,1943
|
||||||
|
59.57,698.4,60.5,Chrysler,1944
|
||||||
|
88.78,846.4,54.6,Chrysler,1945
|
||||||
|
74.12,893.8,84.8,Chrysler,1946
|
||||||
|
62.68,579,96.8,Chrysler,1947
|
||||||
|
89.36,694.6,110.2,Chrysler,1948
|
||||||
|
78.98,590.3,147.4,Chrysler,1949
|
||||||
|
100.66,693.5,163.2,Chrysler,1950
|
||||||
|
160.62,809,203.5,Chrysler,1951
|
||||||
|
145,727,290.6,Chrysler,1952
|
||||||
|
174.93,1001.5,346.1,Chrysler,1953
|
||||||
|
172.49,703.2,414.9,Chrysler,1954
|
||||||
|
39.68,157.7,183.2,Atlantic Refining,1935
|
||||||
|
50.73,167.9,204,Atlantic Refining,1936
|
||||||
|
74.24,192.9,236,Atlantic Refining,1937
|
||||||
|
53.51,156.7,291.7,Atlantic Refining,1938
|
||||||
|
42.65,191.4,323.1,Atlantic Refining,1939
|
||||||
|
46.48,185.5,344,Atlantic Refining,1940
|
||||||
|
61.4,199.6,367.7,Atlantic Refining,1941
|
||||||
|
39.67,189.5,407.2,Atlantic Refining,1942
|
||||||
|
62.24,151.2,426.6,Atlantic Refining,1943
|
||||||
|
52.32,187.7,470,Atlantic Refining,1944
|
||||||
|
63.21,214.7,499.2,Atlantic Refining,1945
|
||||||
|
59.37,232.9,534.6,Atlantic Refining,1946
|
||||||
|
58.02,249,566.6,Atlantic Refining,1947
|
||||||
|
70.34,224.5,595.3,Atlantic Refining,1948
|
||||||
|
67.42,237.3,631.4,Atlantic Refining,1949
|
||||||
|
55.74,240.1,662.3,Atlantic Refining,1950
|
||||||
|
80.3,327.3,683.9,Atlantic Refining,1951
|
||||||
|
85.4,359.4,729.3,Atlantic Refining,1952
|
||||||
|
91.9,398.4,774.3,Atlantic Refining,1953
|
||||||
|
81.43,365.7,804.9,Atlantic Refining,1954
|
||||||
|
20.36,197,6.5,IBM,1935
|
||||||
|
25.98,210.3,15.8,IBM,1936
|
||||||
|
25.94,223.1,27.7,IBM,1937
|
||||||
|
27.53,216.7,39.2,IBM,1938
|
||||||
|
24.6,286.4,48.6,IBM,1939
|
||||||
|
28.54,298,52.5,IBM,1940
|
||||||
|
43.41,276.9,61.5,IBM,1941
|
||||||
|
42.81,272.6,80.5,IBM,1942
|
||||||
|
27.84,287.4,94.4,IBM,1943
|
||||||
|
32.6,330.3,92.6,IBM,1944
|
||||||
|
39.03,324.4,92.3,IBM,1945
|
||||||
|
50.17,401.9,94.2,IBM,1946
|
||||||
|
51.85,407.4,111.4,IBM,1947
|
||||||
|
64.03,409.2,127.4,IBM,1948
|
||||||
|
68.16,482.2,149.3,IBM,1949
|
||||||
|
77.34,673.8,164.4,IBM,1950
|
||||||
|
95.3,676.9,177.2,IBM,1951
|
||||||
|
99.49,702,200,IBM,1952
|
||||||
|
127.52,793.5,211.5,IBM,1953
|
||||||
|
135.72,927.3,238.7,IBM,1954
|
||||||
|
24.43,138,100.2,Union Oil,1935
|
||||||
|
23.21,200.1,125,Union Oil,1936
|
||||||
|
32.78,210.1,142.4,Union Oil,1937
|
||||||
|
32.54,161.2,165.1,Union Oil,1938
|
||||||
|
26.65,161.7,194.8,Union Oil,1939
|
||||||
|
33.71,145.1,222.9,Union Oil,1940
|
||||||
|
43.5,110.6,252.1,Union Oil,1941
|
||||||
|
34.46,98.1,276.3,Union Oil,1942
|
||||||
|
44.28,108.8,300.3,Union Oil,1943
|
||||||
|
70.8,118.2,318.2,Union Oil,1944
|
||||||
|
44.12,126.5,336.2,Union Oil,1945
|
||||||
|
48.98,156.7,351.2,Union Oil,1946
|
||||||
|
48.51,119.4,373.6,Union Oil,1947
|
||||||
|
50,129.1,389.4,Union Oil,1948
|
||||||
|
50.59,134.8,406.7,Union Oil,1949
|
||||||
|
42.53,140.8,429.5,Union Oil,1950
|
||||||
|
64.77,179,450.6,Union Oil,1951
|
||||||
|
72.68,178.1,466.9,Union Oil,1952
|
||||||
|
73.86,186.8,486.2,Union Oil,1953
|
||||||
|
89.51,192.7,511.3,Union Oil,1954
|
||||||
|
12.93,191.5,1.8,Westinghouse,1935
|
||||||
|
25.9,516,0.8,Westinghouse,1936
|
||||||
|
35.05,729,7.4,Westinghouse,1937
|
||||||
|
22.89,560.4,18.1,Westinghouse,1938
|
||||||
|
18.84,519.9,23.5,Westinghouse,1939
|
||||||
|
28.57,628.5,26.5,Westinghouse,1940
|
||||||
|
48.51,537.1,36.2,Westinghouse,1941
|
||||||
|
43.34,561.2,60.8,Westinghouse,1942
|
||||||
|
37.02,617.2,84.4,Westinghouse,1943
|
||||||
|
37.81,626.7,91.2,Westinghouse,1944
|
||||||
|
39.27,737.2,92.4,Westinghouse,1945
|
||||||
|
53.46,760.5,86,Westinghouse,1946
|
||||||
|
55.56,581.4,111.1,Westinghouse,1947
|
||||||
|
49.56,662.3,130.6,Westinghouse,1948
|
||||||
|
32.04,583.8,141.8,Westinghouse,1949
|
||||||
|
32.24,635.2,136.7,Westinghouse,1950
|
||||||
|
54.38,723.8,129.7,Westinghouse,1951
|
||||||
|
71.78,864.1,145.5,Westinghouse,1952
|
||||||
|
90.08,1193.5,174.8,Westinghouse,1953
|
||||||
|
68.6,1188.9,213.5,Westinghouse,1954
|
||||||
|
26.63,290.6,162,Goodyear,1935
|
||||||
|
23.39,291.1,174,Goodyear,1936
|
||||||
|
30.65,335,183,Goodyear,1937
|
||||||
|
20.89,246,198,Goodyear,1938
|
||||||
|
28.78,356.2,208,Goodyear,1939
|
||||||
|
26.93,289.8,223,Goodyear,1940
|
||||||
|
32.08,268.2,234,Goodyear,1941
|
||||||
|
32.21,213.3,248,Goodyear,1942
|
||||||
|
35.69,348.2,274,Goodyear,1943
|
||||||
|
62.47,374.2,282,Goodyear,1944
|
||||||
|
52.32,387.2,316,Goodyear,1945
|
||||||
|
56.95,347.4,302,Goodyear,1946
|
||||||
|
54.32,291.9,333,Goodyear,1947
|
||||||
|
40.53,297.2,359,Goodyear,1948
|
||||||
|
32.54,276.9,370,Goodyear,1949
|
||||||
|
43.48,274.6,376,Goodyear,1950
|
||||||
|
56.49,339.9,391,Goodyear,1951
|
||||||
|
65.98,474.8,414,Goodyear,1952
|
||||||
|
66.11,496,443,Goodyear,1953
|
||||||
|
49.34,474.5,468,Goodyear,1954
|
||||||
|
2.54,70.91,4.5,Diamond Match,1935
|
||||||
|
2,87.94,4.71,Diamond Match,1936
|
||||||
|
2.19,82.2,4.57,Diamond Match,1937
|
||||||
|
1.99,58.72,4.56,Diamond Match,1938
|
||||||
|
2.03,80.54,4.38,Diamond Match,1939
|
||||||
|
1.81,86.47,4.21,Diamond Match,1940
|
||||||
|
2.14,77.68,4.12,Diamond Match,1941
|
||||||
|
1.86,62.16,3.83,Diamond Match,1942
|
||||||
|
0.93,62.24,3.58,Diamond Match,1943
|
||||||
|
1.18,61.82,3.41,Diamond Match,1944
|
||||||
|
1.36,65.85,3.31,Diamond Match,1945
|
||||||
|
2.24,69.54,3.23,Diamond Match,1946
|
||||||
|
3.81,64.97,3.9,Diamond Match,1947
|
||||||
|
5.66,68,5.38,Diamond Match,1948
|
||||||
|
4.21,71.24,7.39,Diamond Match,1949
|
||||||
|
3.42,69.05,8.74,Diamond Match,1950
|
||||||
|
4.67,83.04,9.07,Diamond Match,1951
|
||||||
|
6,74.42,9.93,Diamond Match,1952
|
||||||
|
6.53,63.51,11.68,Diamond Match,1953
|
||||||
|
5.12,58.12,14.33,Diamond Match,1954
|
||||||
|
2.938,30.284,52.011,American Steel,1935
|
||||||
|
5.643,43.909,52.903,American Steel,1936
|
||||||
|
10.233,107.02,54.499,American Steel,1937
|
||||||
|
4.046,68.306,59.722,American Steel,1938
|
||||||
|
3.326,84.164,61.659,American Steel,1939
|
||||||
|
4.68,69.157,62.243,American Steel,1940
|
||||||
|
5.732,60.148,63.361,American Steel,1941
|
||||||
|
12.117,49.332,64.861,American Steel,1942
|
||||||
|
15.276,75.18,67.953,American Steel,1943
|
||||||
|
9.275,62.05,69.59,American Steel,1944
|
||||||
|
9.577,59.152,69.144,American Steel,1945
|
||||||
|
3.956,68.424,70.269,American Steel,1946
|
||||||
|
3.834,48.505,71.051,American Steel,1947
|
||||||
|
5.97,40.507,71.508,American Steel,1948
|
||||||
|
6.433,39.961,73.827,American Steel,1949
|
||||||
|
4.77,36.494,75.847,American Steel,1950
|
||||||
|
6.532,46.082,77.367,American Steel,1951
|
||||||
|
7.329,57.616,78.631,American Steel,1952
|
||||||
|
9.02,57.441,80.215,American Steel,1953
|
||||||
|
6.281,47.165,83.788,American Steel,1954
|
||||||
|
@ -0,0 +1,221 @@
|
|||||||
|
invest,value,capital,firm,year
|
||||||
|
317.6,3078.5,2.8,General Motors,1935
|
||||||
|
391.8,4661.7,52.6,General Motors,1936
|
||||||
|
410.6,5387.1,156.9,General Motors,1937
|
||||||
|
257.7,2792.2,209.2,General Motors,1938
|
||||||
|
330.8,4313.2,203.4,General Motors,1939
|
||||||
|
461.2,4643.9,207.2,General Motors,1940
|
||||||
|
512,4551.2,255.2,General Motors,1941
|
||||||
|
448,3244.1,303.7,General Motors,1942
|
||||||
|
499.6,4053.7,264.1,General Motors,1943
|
||||||
|
547.5,4379.3,201.6,General Motors,1944
|
||||||
|
561.2,4840.9,265,General Motors,1945
|
||||||
|
688.1,4900.9,402.2,General Motors,1946
|
||||||
|
568.9,3526.5,761.5,General Motors,1947
|
||||||
|
529.2,3254.7,922.4,General Motors,1948
|
||||||
|
555.1,3700.2,1020.1,General Motors,1949
|
||||||
|
642.9,3755.6,1099,General Motors,1950
|
||||||
|
755.9,4833,1207.7,General Motors,1951
|
||||||
|
891.2,4924.9,1430.5,General Motors,1952
|
||||||
|
1304.4,6241.7,1777.3,General Motors,1953
|
||||||
|
1486.7,5593.6,2226.3,General Motors,1954
|
||||||
|
209.9,1362.4,53.8,US Steel,1935
|
||||||
|
355.3,1807.1,50.5,US Steel,1936
|
||||||
|
469.9,2676.3,118.1,US Steel,1937
|
||||||
|
262.3,1801.9,260.2,US Steel,1938
|
||||||
|
230.4,1957.3,312.7,US Steel,1939
|
||||||
|
361.6,2202.9,254.2,US Steel,1940
|
||||||
|
472.8,2380.5,261.4,US Steel,1941
|
||||||
|
445.6,2168.6,298.7,US Steel,1942
|
||||||
|
361.6,1985.1,301.8,US Steel,1943
|
||||||
|
288.2,1813.9,279.1,US Steel,1944
|
||||||
|
258.7,1850.2,213.8,US Steel,1945
|
||||||
|
420.3,2067.7,132.6,US Steel,1946
|
||||||
|
420.5,1796.7,264.8,US Steel,1947
|
||||||
|
494.5,1625.8,306.9,US Steel,1948
|
||||||
|
405.1,1667,351.1,US Steel,1949
|
||||||
|
418.8,1677.4,357.8,US Steel,1950
|
||||||
|
588.2,2289.5,342.1,US Steel,1951
|
||||||
|
645.5,2159.4,444.2,US Steel,1952
|
||||||
|
641,2031.3,623.6,US Steel,1953
|
||||||
|
459.3,2115.5,669.7,US Steel,1954
|
||||||
|
33.1,1170.6,97.8,General Electric,1935
|
||||||
|
45,2015.8,104.4,General Electric,1936
|
||||||
|
77.2,2803.3,118,General Electric,1937
|
||||||
|
44.6,2039.7,156.2,General Electric,1938
|
||||||
|
48.1,2256.2,172.6,General Electric,1939
|
||||||
|
74.4,2132.2,186.6,General Electric,1940
|
||||||
|
113,1834.1,220.9,General Electric,1941
|
||||||
|
91.9,1588,287.8,General Electric,1942
|
||||||
|
61.3,1749.4,319.9,General Electric,1943
|
||||||
|
56.8,1687.2,321.3,General Electric,1944
|
||||||
|
93.6,2007.7,319.6,General Electric,1945
|
||||||
|
159.9,2208.3,346,General Electric,1946
|
||||||
|
147.2,1656.7,456.4,General Electric,1947
|
||||||
|
146.3,1604.4,543.4,General Electric,1948
|
||||||
|
98.3,1431.8,618.3,General Electric,1949
|
||||||
|
93.5,1610.5,647.4,General Electric,1950
|
||||||
|
135.2,1819.4,671.3,General Electric,1951
|
||||||
|
157.3,2079.7,726.1,General Electric,1952
|
||||||
|
179.5,2371.6,800.3,General Electric,1953
|
||||||
|
189.6,2759.9,888.9,General Electric,1954
|
||||||
|
40.29,417.5,10.5,Chrysler,1935
|
||||||
|
72.76,837.8,10.2,Chrysler,1936
|
||||||
|
66.26,883.9,34.7,Chrysler,1937
|
||||||
|
51.6,437.9,51.8,Chrysler,1938
|
||||||
|
52.41,679.7,64.3,Chrysler,1939
|
||||||
|
69.41,727.8,67.1,Chrysler,1940
|
||||||
|
68.35,643.6,75.2,Chrysler,1941
|
||||||
|
46.8,410.9,71.4,Chrysler,1942
|
||||||
|
47.4,588.4,67.1,Chrysler,1943
|
||||||
|
59.57,698.4,60.5,Chrysler,1944
|
||||||
|
88.78,846.4,54.6,Chrysler,1945
|
||||||
|
74.12,893.8,84.8,Chrysler,1946
|
||||||
|
62.68,579,96.8,Chrysler,1947
|
||||||
|
89.36,694.6,110.2,Chrysler,1948
|
||||||
|
78.98,590.3,147.4,Chrysler,1949
|
||||||
|
100.66,693.5,163.2,Chrysler,1950
|
||||||
|
160.62,809,203.5,Chrysler,1951
|
||||||
|
145,727,290.6,Chrysler,1952
|
||||||
|
174.93,1001.5,346.1,Chrysler,1953
|
||||||
|
172.49,703.2,414.9,Chrysler,1954
|
||||||
|
39.68,157.7,183.2,Atlantic Refining,1935
|
||||||
|
50.73,167.9,204,Atlantic Refining,1936
|
||||||
|
74.24,192.9,236,Atlantic Refining,1937
|
||||||
|
53.51,156.7,291.7,Atlantic Refining,1938
|
||||||
|
42.65,191.4,323.1,Atlantic Refining,1939
|
||||||
|
46.48,185.5,344,Atlantic Refining,1940
|
||||||
|
61.4,199.6,367.7,Atlantic Refining,1941
|
||||||
|
39.67,189.5,407.2,Atlantic Refining,1942
|
||||||
|
62.24,151.2,426.6,Atlantic Refining,1943
|
||||||
|
52.32,187.7,470,Atlantic Refining,1944
|
||||||
|
63.21,214.7,499.2,Atlantic Refining,1945
|
||||||
|
59.37,232.9,534.6,Atlantic Refining,1946
|
||||||
|
58.02,249,566.6,Atlantic Refining,1947
|
||||||
|
70.34,224.5,595.3,Atlantic Refining,1948
|
||||||
|
67.42,237.3,631.4,Atlantic Refining,1949
|
||||||
|
55.74,240.1,662.3,Atlantic Refining,1950
|
||||||
|
80.3,327.3,683.9,Atlantic Refining,1951
|
||||||
|
85.4,359.4,729.3,Atlantic Refining,1952
|
||||||
|
91.9,398.4,774.3,Atlantic Refining,1953
|
||||||
|
81.43,365.7,804.9,Atlantic Refining,1954
|
||||||
|
20.36,197,6.5,IBM,1935
|
||||||
|
25.98,210.3,15.8,IBM,1936
|
||||||
|
25.94,223.1,27.7,IBM,1937
|
||||||
|
27.53,216.7,39.2,IBM,1938
|
||||||
|
24.6,286.4,48.6,IBM,1939
|
||||||
|
28.54,298,52.5,IBM,1940
|
||||||
|
43.41,276.9,61.5,IBM,1941
|
||||||
|
42.81,272.6,80.5,IBM,1942
|
||||||
|
27.84,287.4,94.4,IBM,1943
|
||||||
|
32.6,330.3,92.6,IBM,1944
|
||||||
|
39.03,324.4,92.3,IBM,1945
|
||||||
|
50.17,401.9,94.2,IBM,1946
|
||||||
|
51.85,407.4,111.4,IBM,1947
|
||||||
|
64.03,409.2,127.4,IBM,1948
|
||||||
|
68.16,482.2,149.3,IBM,1949
|
||||||
|
77.34,673.8,164.4,IBM,1950
|
||||||
|
95.3,676.9,177.2,IBM,1951
|
||||||
|
99.49,702,200,IBM,1952
|
||||||
|
127.52,793.5,211.5,IBM,1953
|
||||||
|
135.72,927.3,238.7,IBM,1954
|
||||||
|
24.43,138,100.2,Union Oil,1935
|
||||||
|
23.21,200.1,125,Union Oil,1936
|
||||||
|
32.78,210.1,142.4,Union Oil,1937
|
||||||
|
32.54,161.2,165.1,Union Oil,1938
|
||||||
|
26.65,161.7,194.8,Union Oil,1939
|
||||||
|
33.71,145.1,222.9,Union Oil,1940
|
||||||
|
43.5,110.6,252.1,Union Oil,1941
|
||||||
|
34.46,98.1,276.3,Union Oil,1942
|
||||||
|
44.28,108.8,300.3,Union Oil,1943
|
||||||
|
70.8,118.2,318.2,Union Oil,1944
|
||||||
|
44.12,126.5,336.2,Union Oil,1945
|
||||||
|
48.98,156.7,351.2,Union Oil,1946
|
||||||
|
48.51,119.4,373.6,Union Oil,1947
|
||||||
|
50,129.1,389.4,Union Oil,1948
|
||||||
|
50.59,134.8,406.7,Union Oil,1949
|
||||||
|
42.53,140.8,429.5,Union Oil,1950
|
||||||
|
64.77,179,450.6,Union Oil,1951
|
||||||
|
72.68,178.1,466.9,Union Oil,1952
|
||||||
|
73.86,186.8,486.2,Union Oil,1953
|
||||||
|
89.51,192.7,511.3,Union Oil,1954
|
||||||
|
12.93,191.5,1.8,Westinghouse,1935
|
||||||
|
25.9,516,0.8,Westinghouse,1936
|
||||||
|
35.05,729,7.4,Westinghouse,1937
|
||||||
|
22.89,560.4,18.1,Westinghouse,1938
|
||||||
|
18.84,519.9,23.5,Westinghouse,1939
|
||||||
|
28.57,628.5,26.5,Westinghouse,1940
|
||||||
|
48.51,537.1,36.2,Westinghouse,1941
|
||||||
|
43.34,561.2,60.8,Westinghouse,1942
|
||||||
|
37.02,617.2,84.4,Westinghouse,1943
|
||||||
|
37.81,626.7,91.2,Westinghouse,1944
|
||||||
|
39.27,737.2,92.4,Westinghouse,1945
|
||||||
|
53.46,760.5,86,Westinghouse,1946
|
||||||
|
55.56,581.4,111.1,Westinghouse,1947
|
||||||
|
49.56,662.3,130.6,Westinghouse,1948
|
||||||
|
32.04,583.8,141.8,Westinghouse,1949
|
||||||
|
32.24,635.2,136.7,Westinghouse,1950
|
||||||
|
54.38,723.8,129.7,Westinghouse,1951
|
||||||
|
71.78,864.1,145.5,Westinghouse,1952
|
||||||
|
90.08,1193.5,174.8,Westinghouse,1953
|
||||||
|
68.6,1188.9,213.5,Westinghouse,1954
|
||||||
|
26.63,290.6,162,Goodyear,1935
|
||||||
|
23.39,291.1,174,Goodyear,1936
|
||||||
|
30.65,335,183,Goodyear,1937
|
||||||
|
20.89,246,198,Goodyear,1938
|
||||||
|
28.78,356.2,208,Goodyear,1939
|
||||||
|
26.93,289.8,223,Goodyear,1940
|
||||||
|
32.08,268.2,234,Goodyear,1941
|
||||||
|
32.21,213.3,248,Goodyear,1942
|
||||||
|
35.69,348.2,274,Goodyear,1943
|
||||||
|
62.47,374.2,282,Goodyear,1944
|
||||||
|
52.32,387.2,316,Goodyear,1945
|
||||||
|
56.95,347.4,302,Goodyear,1946
|
||||||
|
54.32,291.9,333,Goodyear,1947
|
||||||
|
40.53,297.2,359,Goodyear,1948
|
||||||
|
32.54,276.9,370,Goodyear,1949
|
||||||
|
43.48,274.6,376,Goodyear,1950
|
||||||
|
56.49,339.9,391,Goodyear,1951
|
||||||
|
65.98,474.8,414,Goodyear,1952
|
||||||
|
66.11,496,443,Goodyear,1953
|
||||||
|
49.34,474.5,468,Goodyear,1954
|
||||||
|
2.54,70.91,4.5,Diamond Match,1935
|
||||||
|
2,87.94,4.71,Diamond Match,1936
|
||||||
|
2.19,82.2,4.57,Diamond Match,1937
|
||||||
|
1.99,58.72,4.56,Diamond Match,1938
|
||||||
|
2.03,80.54,4.38,Diamond Match,1939
|
||||||
|
1.81,86.47,4.21,Diamond Match,1940
|
||||||
|
2.14,77.68,4.12,Diamond Match,1941
|
||||||
|
1.86,62.16,3.83,Diamond Match,1942
|
||||||
|
0.93,62.24,3.58,Diamond Match,1943
|
||||||
|
1.18,61.82,3.41,Diamond Match,1944
|
||||||
|
1.36,65.85,3.31,Diamond Match,1945
|
||||||
|
2.24,69.54,3.23,Diamond Match,1946
|
||||||
|
3.81,64.97,3.9,Diamond Match,1947
|
||||||
|
5.66,68,5.38,Diamond Match,1948
|
||||||
|
4.21,71.24,7.39,Diamond Match,1949
|
||||||
|
3.42,69.05,8.74,Diamond Match,1950
|
||||||
|
4.67,83.04,9.07,Diamond Match,1951
|
||||||
|
6,74.42,9.93,Diamond Match,1952
|
||||||
|
6.53,63.51,11.68,Diamond Match,1953
|
||||||
|
5.12,58.12,14.33,Diamond Match,1954
|
||||||
|
2.938,30.284,52.011,American Steel,1935
|
||||||
|
5.643,43.909,52.903,American Steel,1936
|
||||||
|
10.233,107.02,54.499,American Steel,1937
|
||||||
|
4.046,68.306,59.722,American Steel,1938
|
||||||
|
3.326,84.164,61.659,American Steel,1939
|
||||||
|
4.68,69.157,62.243,American Steel,1940
|
||||||
|
5.732,60.148,63.361,American Steel,1941
|
||||||
|
12.117,49.332,64.861,American Steel,1942
|
||||||
|
15.276,75.18,67.953,American Steel,1943
|
||||||
|
9.275,62.05,69.59,American Steel,1944
|
||||||
|
9.577,59.152,69.144,American Steel,1945
|
||||||
|
3.956,68.424,70.269,American Steel,1946
|
||||||
|
3.834,48.505,71.051,American Steel,1947
|
||||||
|
5.97,40.507,71.508,American Steel,1948
|
||||||
|
6.433,39.961,73.827,American Steel,1949
|
||||||
|
4.77,36.494,75.847,American Steel,1950
|
||||||
|
6.532,46.082,77.367,American Steel,1951
|
||||||
|
7.329,57.616,78.631,American Steel,1952
|
||||||
|
9.02,57.441,80.215,American Steel,1953
|
||||||
|
6.281,47.165,83.788,American Steel,1954
|
||||||
|
@ -0,0 +1,37 @@
|
|||||||
|
### GLS Example with Longley Data
|
||||||
|
### Done the long way...
|
||||||
|
|
||||||
|
d <- read.table('./longley.csv', sep=',', header=T)
|
||||||
|
attach(d)
|
||||||
|
m1 <- lm(TOTEMP ~ GNP + POP)
|
||||||
|
rho <- cor(m1$res[-1],m1$res[-16])
|
||||||
|
sigma <- diag(16) # diagonal matrix of ones
|
||||||
|
sigma <- rho^abs(row(sigma)-col(sigma))
|
||||||
|
# row sigma is a matrix of the row index
|
||||||
|
# col sigma is a matrix of the column index
|
||||||
|
# this gives a upper-lower triangle with the
|
||||||
|
# covariance structure of an AR1 process...
|
||||||
|
sigma_inv <- solve(sigma) # inverse of sigma
|
||||||
|
x <- model.matrix(m1)
|
||||||
|
xPrimexInv <- solve(t(x) %*% sigma_inv %*% x)
|
||||||
|
beta <- xPrimexInv %*% t(x) %*% sigma_inv %*% TOTEMP
|
||||||
|
beta
|
||||||
|
# residuals
|
||||||
|
res <- TOTEMP - x %*% beta
|
||||||
|
# whitened residuals, not sure if this is right
|
||||||
|
# xPrimexInv is different than cholsigmainv obviously...
|
||||||
|
wres = sigma_inv %*% TOTEMP - sigma_inv %*% x %*% beta
|
||||||
|
|
||||||
|
sig <- sqrt(sum(res^2)/m1$df)
|
||||||
|
wsig <- sqrt(sum(wres^2)/m1$df)
|
||||||
|
wvc <- sqrt(diag(xPrimexInv))*wsig
|
||||||
|
vc <- sqrt(diag(xPrimexInv))*sig
|
||||||
|
vc
|
||||||
|
|
||||||
|
### Attempt to use a varFunc for GLS
|
||||||
|
library(nlme)
|
||||||
|
m1 <- gls(TOTEMP ~ GNP + POP, correlation=corAR1(value=rho, fixed=TRUE))
|
||||||
|
results <- summary(m1)
|
||||||
|
bse <- sqrt(diag(vcov(m1)))
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,6 @@
|
|||||||
|
d <- read.table('./longley.csv', sep=',', header=T)
|
||||||
|
attach(d)
|
||||||
|
|
||||||
|
library(nlme) # to be able to get BIC
|
||||||
|
m1 <- lm(TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR)
|
||||||
|
results <-summary(m1)
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,73 @@
|
|||||||
|
"""Longley dataset"""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """This is public domain."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
The classic 1967 Longley Data
|
||||||
|
|
||||||
|
http://www.itl.nist.gov/div898/strd/lls/data/Longley.shtml
|
||||||
|
|
||||||
|
::
|
||||||
|
|
||||||
|
Longley, J.W. (1967) "An Appraisal of Least Squares Programs for the
|
||||||
|
Electronic Comptuer from the Point of View of the User." Journal of
|
||||||
|
the American Statistical Association. 62.319, 819-41.
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """"""
|
||||||
|
|
||||||
|
DESCRLONG = """The Longley dataset contains various US macroeconomic
|
||||||
|
variables that are known to be highly collinear. It has been used to appraise
|
||||||
|
the accuracy of least squares routines."""
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of Observations - 16
|
||||||
|
|
||||||
|
Number of Variables - 6
|
||||||
|
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
TOTEMP - Total Employment
|
||||||
|
GNPDEFL - GNP deflator
|
||||||
|
GNP - GNP
|
||||||
|
UNEMP - Number of unemployed
|
||||||
|
ARMED - Size of armed forces
|
||||||
|
POP - Population
|
||||||
|
YEAR - Year (1947 - 1962)
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, array, column_stack
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Load the Longley data and return a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""
|
||||||
|
Load the Longley data and return a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=0)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath+'/longley.csv',"rb"), delimiter=",",
|
||||||
|
names=True, dtype=float, usecols=(1,2,3,4,5,6,7))
|
||||||
|
return data
|
||||||
@ -0,0 +1,17 @@
|
|||||||
|
"Obs","TOTEMP","GNPDEFL","GNP","UNEMP","ARMED","POP","YEAR"
|
||||||
|
1,60323,83,234289,2356,1590,107608,1947
|
||||||
|
2,61122,88.5,259426,2325,1456,108632,1948
|
||||||
|
3,60171,88.2,258054,3682,1616,109773,1949
|
||||||
|
4,61187,89.5,284599,3351,1650,110929,1950
|
||||||
|
5,63221,96.2,328975,2099,3099,112075,1951
|
||||||
|
6,63639,98.1,346999,1932,3594,113270,1952
|
||||||
|
7,64989,99,365385,1870,3547,115094,1953
|
||||||
|
8,63761,100,363112,3578,3350,116219,1954
|
||||||
|
9,66019,101.2,397469,2904,3048,117388,1955
|
||||||
|
10,67857,104.6,419180,2822,2857,118734,1956
|
||||||
|
11,68169,108.4,442769,2936,2798,120445,1957
|
||||||
|
12,66513,110.8,444546,4681,2637,121950,1958
|
||||||
|
13,68655,112.6,482704,3813,2552,123366,1959
|
||||||
|
14,69564,114.2,502601,3931,2514,125368,1960
|
||||||
|
15,69331,115.7,518173,4806,2572,127852,1961
|
||||||
|
16,70551,116.9,554894,4007,2827,130081,1962
|
||||||
|
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,89 @@
|
|||||||
|
"""United States Macroeconomic data"""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """This is public domain."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
Compiled by Skipper Seabold. All data are from the Federal Reserve Bank of St.
|
||||||
|
Louis [1] except the unemployment rate which was taken from the National
|
||||||
|
Bureau of Labor Statistics [2]. ::
|
||||||
|
|
||||||
|
[1] Data Source: FRED, Federal Reserve Economic Data, Federal Reserve Bank of
|
||||||
|
St. Louis; http://research.stlouisfed.org/fred2/; accessed December 15,
|
||||||
|
2009.
|
||||||
|
|
||||||
|
[2] Data Source: Bureau of Labor Statistics, U.S. Department of Labor;
|
||||||
|
http://www.bls.gov/data/; accessed December 15, 2009.
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """US Macroeconomic Data for 1959Q1 - 2009Q3"""
|
||||||
|
|
||||||
|
DESCRLONG = DESCRSHORT
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of Observations - 203
|
||||||
|
|
||||||
|
Number of Variables - 14
|
||||||
|
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
year - 1959q1 - 2009q3
|
||||||
|
quarter - 1-4
|
||||||
|
realgdp - Real gross domestic product (Bil. of chained 2005 US$,
|
||||||
|
seasonally adjusted annual rate)
|
||||||
|
realcons - Real personal consumption expenditures (Bil. of chained 2005
|
||||||
|
US$,
|
||||||
|
seasonally adjusted annual rate)
|
||||||
|
realinv - Real gross private domestic investment (Bil. of chained 2005
|
||||||
|
US$, seasonally adjusted annual rate)
|
||||||
|
realgovt - Real federal consumption expenditures & gross investment
|
||||||
|
(Bil. of chained 2005 US$, seasonally adjusted annual rate)
|
||||||
|
realdpi - Real gross private domestic investment (Bil. of chained 2005
|
||||||
|
US$, seasonally adjusted annual rate)
|
||||||
|
cpi - End of the quarter consumer price index for all urban
|
||||||
|
consumers: all items (1982-84 = 100, seasonally adjusted).
|
||||||
|
m1 - End of the quarter M1 nominal money stock (Seasonally adjusted)
|
||||||
|
tbilrate - Quarterly monthly average of the monthly 3-month treasury bill:
|
||||||
|
secondary market rate
|
||||||
|
unemp - Seasonally adjusted unemployment rate (%)
|
||||||
|
pop - End of the quarter total population: all ages incl. armed
|
||||||
|
forces over seas
|
||||||
|
infl - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)
|
||||||
|
realint - Real interest rate (tbilrate - infl)
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
from pandas import DataFrame
|
||||||
|
|
||||||
|
from scikits.statsmodels.tools import Dataset
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Load the US macro data and return a Dataset class.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
|
||||||
|
Notes
|
||||||
|
-----
|
||||||
|
The macrodata Dataset instance does not contain endog and exog attributes.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
names = data.dtype.names
|
||||||
|
dataset = Dataset(data=data, names=names)
|
||||||
|
return dataset
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
dataset = load()
|
||||||
|
dataset.data = DataFrame(dataset.data)
|
||||||
|
return dataset
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/macrodata.csv', 'rb'), delimiter=",",
|
||||||
|
names=True, dtype=float)
|
||||||
|
return data
|
||||||
@ -0,0 +1,204 @@
|
|||||||
|
"year","quarter","realgdp","realcons","realinv","realgovt","realdpi","cpi","m1","tbilrate","unemp","pop","infl","realint"
|
||||||
|
1959,1,2710.349,1707.4,286.898,470.045,1886.9,28.980,139.7,2.82,5.8,177.146,0,0
|
||||||
|
1959,2,2778.801,1733.7,310.859,481.301,1919.7,29.150,141.7,3.08,5.1,177.830,2.34,0.74
|
||||||
|
1959,3,2775.488,1751.8,289.226,491.260,1916.4,29.350,140.5,3.82,5.3,178.657,2.74,1.09
|
||||||
|
1959,4,2785.204,1753.7,299.356,484.052,1931.3,29.370,140,4.33,5.6,179.386,0.27,4.06
|
||||||
|
1960,1,2847.699,1770.5,331.722,462.199,1955.5,29.540,139.6,3.50,5.2,180.007,2.31,1.19
|
||||||
|
1960,2,2834.390,1792.9,298.152,460.400,1966.1,29.550,140.2,2.68,5.2,180.671,0.14,2.55
|
||||||
|
1960,3,2839.022,1785.8,296.375,474.676,1967.8,29.750,140.9,2.36,5.6,181.528,2.7,-0.34
|
||||||
|
1960,4,2802.616,1788.2,259.764,476.434,1966.6,29.840,141.1,2.29,6.3,182.287,1.21,1.08
|
||||||
|
1961,1,2819.264,1787.7,266.405,475.854,1984.5,29.810,142.1,2.37,6.8,182.992,-0.4,2.77
|
||||||
|
1961,2,2872.005,1814.3,286.246,480.328,2014.4,29.920,142.9,2.29,7,183.691,1.47,0.81
|
||||||
|
1961,3,2918.419,1823.1,310.227,493.828,2041.9,29.980,144.1,2.32,6.8,184.524,0.8,1.52
|
||||||
|
1961,4,2977.830,1859.6,315.463,502.521,2082.0,30.040,145.2,2.60,6.2,185.242,0.8,1.8
|
||||||
|
1962,1,3031.241,1879.4,334.271,520.960,2101.7,30.210,146.4,2.73,5.6,185.874,2.26,0.47
|
||||||
|
1962,2,3064.709,1902.5,331.039,523.066,2125.2,30.220,146.5,2.78,5.5,186.538,0.13,2.65
|
||||||
|
1962,3,3093.047,1917.9,336.962,538.838,2137.0,30.380,146.7,2.78,5.6,187.323,2.11,0.67
|
||||||
|
1962,4,3100.563,1945.1,325.650,535.912,2154.6,30.440,148.3,2.87,5.5,188.013,0.79,2.08
|
||||||
|
1963,1,3141.087,1958.2,343.721,522.917,2172.5,30.480,149.7,2.90,5.8,188.580,0.53,2.38
|
||||||
|
1963,2,3180.447,1976.9,348.730,518.108,2193.1,30.690,151.3,3.03,5.7,189.242,2.75,0.29
|
||||||
|
1963,3,3240.332,2003.8,360.102,546.893,2217.9,30.750,152.6,3.38,5.5,190.028,0.78,2.6
|
||||||
|
1963,4,3264.967,2020.6,364.534,532.383,2254.6,30.940,153.7,3.52,5.6,190.668,2.46,1.06
|
||||||
|
1964,1,3338.246,2060.5,379.523,529.686,2299.6,30.950,154.8,3.51,5.5,191.245,0.13,3.38
|
||||||
|
1964,2,3376.587,2096.7,377.778,526.175,2362.1,31.020,156.8,3.47,5.2,191.889,0.9,2.57
|
||||||
|
1964,3,3422.469,2135.2,386.754,522.008,2392.7,31.120,159.2,3.53,5,192.631,1.29,2.25
|
||||||
|
1964,4,3431.957,2141.2,389.910,514.603,2420.4,31.280,160.7,3.76,5,193.223,2.05,1.71
|
||||||
|
1965,1,3516.251,2188.8,429.145,508.006,2447.4,31.380,162,3.93,4.9,193.709,1.28,2.65
|
||||||
|
1965,2,3563.960,2213.0,429.119,508.931,2474.5,31.580,163.1,3.84,4.7,194.303,2.54,1.3
|
||||||
|
1965,3,3636.285,2251.0,444.444,529.446,2542.6,31.650,166,3.93,4.4,194.997,0.89,3.04
|
||||||
|
1965,4,3724.014,2314.3,446.493,544.121,2594.1,31.880,169.1,4.35,4.1,195.539,2.9,1.46
|
||||||
|
1966,1,3815.423,2348.5,484.244,556.593,2618.4,32.280,171.8,4.62,3.9,195.999,4.99,-0.37
|
||||||
|
1966,2,3828.124,2354.5,475.408,571.371,2624.7,32.450,170.3,4.65,3.8,196.560,2.1,2.55
|
||||||
|
1966,3,3853.301,2381.5,470.697,594.514,2657.8,32.850,171.2,5.23,3.8,197.207,4.9,0.33
|
||||||
|
1966,4,3884.520,2391.4,472.957,599.528,2688.2,32.900,171.9,5.00,3.7,197.736,0.61,4.39
|
||||||
|
1967,1,3918.740,2405.3,460.007,640.682,2728.4,33.100,174.2,4.22,3.8,198.206,2.42,1.8
|
||||||
|
1967,2,3919.556,2438.1,440.393,631.430,2750.8,33.400,178.1,3.78,3.8,198.712,3.61,0.17
|
||||||
|
1967,3,3950.826,2450.6,453.033,641.504,2777.1,33.700,181.6,4.42,3.8,199.311,3.58,0.84
|
||||||
|
1967,4,3980.970,2465.7,462.834,640.234,2797.4,34.100,184.3,4.90,3.9,199.808,4.72,0.18
|
||||||
|
1968,1,4063.013,2524.6,472.907,651.378,2846.2,34.400,186.6,5.18,3.7,200.208,3.5,1.67
|
||||||
|
1968,2,4131.998,2563.3,492.026,646.145,2893.5,34.900,190.5,5.50,3.5,200.706,5.77,-0.28
|
||||||
|
1968,3,4160.267,2611.5,476.053,640.615,2899.3,35.300,194,5.21,3.5,201.290,4.56,0.65
|
||||||
|
1968,4,4178.293,2623.5,480.998,636.729,2918.4,35.700,198.7,5.85,3.4,201.760,4.51,1.34
|
||||||
|
1969,1,4244.100,2652.9,512.686,633.224,2923.4,36.300,200.7,6.08,3.4,202.161,6.67,-0.58
|
||||||
|
1969,2,4256.460,2669.8,508.601,623.160,2952.9,36.800,201.7,6.49,3.4,202.677,5.47,1.02
|
||||||
|
1969,3,4283.378,2682.7,520.360,623.613,3012.9,37.300,202.9,7.02,3.6,203.302,5.4,1.63
|
||||||
|
1969,4,4263.261,2704.1,492.334,606.900,3034.9,37.900,206.2,7.64,3.6,203.849,6.38,1.26
|
||||||
|
1970,1,4256.573,2720.7,476.925,594.888,3050.1,38.500,206.7,6.76,4.2,204.401,6.28,0.47
|
||||||
|
1970,2,4264.289,2733.2,478.419,576.257,3103.5,38.900,208,6.66,4.8,205.052,4.13,2.52
|
||||||
|
1970,3,4302.259,2757.1,486.594,567.743,3145.4,39.400,212.9,6.15,5.2,205.788,5.11,1.04
|
||||||
|
1970,4,4256.637,2749.6,458.406,564.666,3135.1,39.900,215.5,4.86,5.8,206.466,5.04,-0.18
|
||||||
|
1971,1,4374.016,2802.2,517.935,542.709,3197.3,40.100,220,3.65,5.9,207.065,2,1.65
|
||||||
|
1971,2,4398.829,2827.9,533.986,534.905,3245.3,40.600,224.9,4.76,5.9,207.661,4.96,-0.19
|
||||||
|
1971,3,4433.943,2850.4,541.010,532.646,3259.7,40.900,227.2,4.70,6,208.345,2.94,1.75
|
||||||
|
1971,4,4446.264,2897.8,524.085,516.140,3294.2,41.200,230.1,3.87,6,208.917,2.92,0.95
|
||||||
|
1972,1,4525.769,2936.5,561.147,518.192,3314.9,41.500,235.6,3.55,5.8,209.386,2.9,0.64
|
||||||
|
1972,2,4633.101,2992.6,595.495,526.473,3346.1,41.800,238.8,3.86,5.7,209.896,2.88,0.98
|
||||||
|
1972,3,4677.503,3038.8,603.970,498.116,3414.6,42.200,245,4.47,5.6,210.479,3.81,0.66
|
||||||
|
1972,4,4754.546,3110.1,607.104,496.540,3550.5,42.700,251.5,5.09,5.3,210.985,4.71,0.38
|
||||||
|
1973,1,4876.166,3167.0,645.654,504.838,3590.7,43.700,252.7,5.98,5,211.420,9.26,-3.28
|
||||||
|
1973,2,4932.571,3165.4,675.837,497.033,3626.2,44.200,257.5,7.19,4.9,211.909,4.55,2.64
|
||||||
|
1973,3,4906.252,3176.7,649.412,475.897,3644.4,45.600,259,8.06,4.8,212.475,12.47,-4.41
|
||||||
|
1973,4,4953.050,3167.4,674.253,476.174,3688.9,46.800,263.8,7.68,4.8,212.932,10.39,-2.71
|
||||||
|
1974,1,4909.617,3139.7,631.230,491.043,3632.3,48.100,267.2,7.80,5.1,213.361,10.96,-3.16
|
||||||
|
1974,2,4922.188,3150.6,628.102,490.177,3601.1,49.300,269.3,7.89,5.2,213.854,9.86,-1.96
|
||||||
|
1974,3,4873.520,3163.6,592.672,492.586,3612.4,51.000,272.3,8.16,5.6,214.451,13.56,-5.4
|
||||||
|
1974,4,4854.340,3117.3,598.306,496.176,3596.0,52.300,273.9,6.96,6.6,214.931,10.07,-3.11
|
||||||
|
1975,1,4795.295,3143.4,493.212,490.603,3581.9,53.000,276.2,5.53,8.2,215.353,5.32,0.22
|
||||||
|
1975,2,4831.942,3195.8,476.085,486.679,3749.3,54.000,283.7,5.57,8.9,215.973,7.48,-1.91
|
||||||
|
1975,3,4913.328,3241.4,516.402,498.836,3698.6,54.900,285.4,6.27,8.5,216.587,6.61,-0.34
|
||||||
|
1975,4,4977.511,3275.7,530.596,500.141,3736.0,55.800,288.4,5.26,8.3,217.095,6.5,-1.24
|
||||||
|
1976,1,5090.663,3341.2,585.541,495.568,3791.0,56.100,294.7,4.91,7.7,217.528,2.14,2.77
|
||||||
|
1976,2,5128.947,3371.8,610.513,494.532,3822.2,57.000,297.2,5.28,7.6,218.035,6.37,-1.09
|
||||||
|
1976,3,5154.072,3407.5,611.646,493.141,3856.7,57.900,302,5.05,7.7,218.644,6.27,-1.22
|
||||||
|
1976,4,5191.499,3451.8,615.898,494.415,3884.4,58.700,308.3,4.57,7.8,219.179,5.49,-0.92
|
||||||
|
1977,1,5251.762,3491.3,646.198,498.509,3887.5,60.000,316,4.60,7.5,219.684,8.76,-4.16
|
||||||
|
1977,2,5356.131,3510.6,696.141,506.695,3931.8,60.800,320.2,5.06,7.1,220.239,5.3,-0.24
|
||||||
|
1977,3,5451.921,3544.1,734.078,509.605,3990.8,61.600,326.4,5.82,6.9,220.904,5.23,0.59
|
||||||
|
1977,4,5450.793,3597.5,713.356,504.584,4071.2,62.700,334.4,6.20,6.6,221.477,7.08,-0.88
|
||||||
|
1978,1,5469.405,3618.5,727.504,506.314,4096.4,63.900,339.9,6.34,6.3,221.991,7.58,-1.24
|
||||||
|
1978,2,5684.569,3695.9,777.454,518.366,4143.4,65.500,347.6,6.72,6,222.585,9.89,-3.18
|
||||||
|
1978,3,5740.300,3711.4,801.452,520.199,4177.1,67.100,353.3,7.64,6,223.271,9.65,-2.01
|
||||||
|
1978,4,5816.222,3741.3,819.689,524.782,4209.8,68.500,358.6,9.02,5.9,223.865,8.26,0.76
|
||||||
|
1979,1,5825.949,3760.2,819.556,525.524,4255.9,70.600,368,9.42,5.9,224.438,12.08,-2.66
|
||||||
|
1979,2,5831.418,3758.0,817.660,532.040,4226.1,73.000,377.2,9.30,5.7,225.055,13.37,-4.07
|
||||||
|
1979,3,5873.335,3794.9,801.742,531.232,4250.3,75.200,380.8,10.49,5.9,225.801,11.88,-1.38
|
||||||
|
1979,4,5889.495,3805.0,786.817,531.126,4284.3,78.000,385.8,11.94,5.9,226.451,14.62,-2.68
|
||||||
|
1980,1,5908.467,3798.4,781.114,548.115,4296.2,80.900,383.8,13.75,6.3,227.061,14.6,-0.85
|
||||||
|
1980,2,5787.373,3712.2,710.640,561.895,4236.1,82.600,394,7.90,7.3,227.726,8.32,-0.42
|
||||||
|
1980,3,5776.617,3752.0,656.477,554.292,4279.7,84.700,409,10.34,7.7,228.417,10.04,0.3
|
||||||
|
1980,4,5883.460,3802.0,723.220,556.130,4368.1,87.200,411.3,14.75,7.4,228.937,11.64,3.11
|
||||||
|
1981,1,6005.717,3822.8,795.091,567.618,4358.1,89.100,427.4,13.95,7.4,229.403,8.62,5.32
|
||||||
|
1981,2,5957.795,3822.8,757.240,584.540,4358.6,91.500,426.9,15.33,7.4,229.966,10.63,4.69
|
||||||
|
1981,3,6030.184,3838.3,804.242,583.890,4455.4,93.400,428.4,14.58,7.4,230.641,8.22,6.36
|
||||||
|
1981,4,5955.062,3809.3,773.053,590.125,4464.4,94.400,442.7,11.33,8.2,231.157,4.26,7.07
|
||||||
|
1982,1,5857.333,3833.9,692.514,591.043,4469.6,95.000,447.1,12.95,8.8,231.645,2.53,10.42
|
||||||
|
1982,2,5889.074,3847.7,691.900,596.403,4500.8,97.500,448,11.97,9.4,232.188,10.39,1.58
|
||||||
|
1982,3,5866.370,3877.2,683.825,605.370,4520.6,98.100,464.5,8.10,9.9,232.816,2.45,5.65
|
||||||
|
1982,4,5871.001,3947.9,622.930,623.307,4536.4,97.900,477.2,7.96,10.7,233.322,-0.82,8.77
|
||||||
|
1983,1,5944.020,3986.6,645.110,630.873,4572.2,98.800,493.2,8.22,10.4,233.781,3.66,4.56
|
||||||
|
1983,2,6077.619,4065.7,707.372,644.322,4605.5,99.800,507.8,8.69,10.1,234.307,4.03,4.66
|
||||||
|
1983,3,6197.468,4137.6,754.937,662.412,4674.7,100.800,517.2,8.99,9.4,234.907,3.99,5.01
|
||||||
|
1983,4,6325.574,4203.2,834.427,639.197,4771.1,102.100,525.1,8.89,8.5,235.385,5.13,3.76
|
||||||
|
1984,1,6448.264,4239.2,921.763,644.635,4875.4,103.300,535,9.43,7.9,235.839,4.67,4.76
|
||||||
|
1984,2,6559.594,4299.9,952.841,664.839,4959.4,104.100,540.9,9.94,7.5,236.348,3.09,6.85
|
||||||
|
1984,3,6623.343,4333.0,974.989,662.294,5036.6,105.100,543.7,10.19,7.4,236.976,3.82,6.37
|
||||||
|
1984,4,6677.264,4390.1,958.993,684.282,5084.5,105.700,557,8.14,7.3,237.468,2.28,5.87
|
||||||
|
1985,1,6740.275,4464.6,927.375,691.613,5072.0,107.000,570.4,8.25,7.3,237.900,4.89,3.36
|
||||||
|
1985,2,6797.344,4505.2,943.383,708.524,5172.7,107.700,589.1,7.17,7.3,238.466,2.61,4.56
|
||||||
|
1985,3,6903.523,4590.8,932.959,732.305,5140.7,108.500,607.8,7.13,7.2,239.113,2.96,4.17
|
||||||
|
1985,4,6955.918,4600.9,969.434,732.026,5193.9,109.900,621.4,7.14,7,239.638,5.13,2.01
|
||||||
|
1986,1,7022.757,4639.3,967.442,728.125,5255.8,108.700,641,6.56,7,240.094,-4.39,10.95
|
||||||
|
1986,2,7050.969,4688.7,945.972,751.334,5315.5,109.500,670.3,6.06,7.2,240.651,2.93,3.13
|
||||||
|
1986,3,7118.950,4770.7,916.315,779.770,5343.3,110.200,694.9,5.31,7,241.274,2.55,2.76
|
||||||
|
1986,4,7153.359,4799.4,917.736,767.671,5346.5,111.400,730.2,5.44,6.8,241.784,4.33,1.1
|
||||||
|
1987,1,7193.019,4792.1,945.776,772.247,5379.4,112.700,743.9,5.61,6.6,242.252,4.64,0.97
|
||||||
|
1987,2,7269.510,4856.3,947.100,782.962,5321.0,113.800,743,5.67,6.3,242.804,3.89,1.79
|
||||||
|
1987,3,7332.558,4910.4,948.055,783.804,5416.2,115.000,756.2,6.19,6,243.446,4.2,1.99
|
||||||
|
1987,4,7458.022,4922.2,1021.980,795.467,5493.1,116.000,756.2,5.76,5.9,243.981,3.46,2.29
|
||||||
|
1988,1,7496.600,5004.4,964.398,773.851,5562.1,117.200,768.1,5.76,5.7,244.445,4.12,1.64
|
||||||
|
1988,2,7592.881,5040.8,987.858,765.980,5614.3,118.500,781.4,6.48,5.5,245.021,4.41,2.07
|
||||||
|
1988,3,7632.082,5080.6,994.204,760.245,5657.5,119.900,783.3,7.22,5.5,245.693,4.7,2.52
|
||||||
|
1988,4,7733.991,5140.4,1007.371,783.065,5708.5,121.200,785.7,8.03,5.3,246.224,4.31,3.72
|
||||||
|
1989,1,7806.603,5159.3,1045.975,767.024,5773.4,123.100,779.2,8.67,5.2,246.721,6.22,2.44
|
||||||
|
1989,2,7865.016,5182.4,1033.753,784.275,5749.8,124.500,777.8,8.15,5.2,247.342,4.52,3.63
|
||||||
|
1989,3,7927.393,5236.1,1021.604,791.819,5787.0,125.400,786.6,7.76,5.3,248.067,2.88,4.88
|
||||||
|
1989,4,7944.697,5261.7,1011.119,787.844,5831.3,127.500,795.4,7.65,5.4,248.659,6.64,1.01
|
||||||
|
1990,1,8027.693,5303.3,1021.070,799.681,5875.1,128.900,806.2,7.80,5.3,249.306,4.37,3.44
|
||||||
|
1990,2,8059.598,5320.8,1021.360,800.639,5913.9,130.500,810.1,7.70,5.3,250.132,4.93,2.76
|
||||||
|
1990,3,8059.476,5341.0,997.319,793.513,5918.1,133.400,819.8,7.33,5.7,251.057,8.79,-1.46
|
||||||
|
1990,4,7988.864,5299.5,934.248,800.525,5878.2,134.700,827.2,6.67,6.1,251.889,3.88,2.79
|
||||||
|
1991,1,7950.164,5284.4,896.210,806.775,5896.3,135.100,843.2,5.83,6.6,252.643,1.19,4.65
|
||||||
|
1991,2,8003.822,5324.7,891.704,809.081,5941.1,136.200,861.5,5.54,6.8,253.493,3.24,2.29
|
||||||
|
1991,3,8037.538,5345.0,913.904,793.987,5953.6,137.200,878,5.18,6.9,254.435,2.93,2.25
|
||||||
|
1991,4,8069.046,5342.6,948.891,778.378,5992.4,138.300,910.4,4.14,7.1,255.214,3.19,0.95
|
||||||
|
1992,1,8157.616,5434.5,927.796,778.568,6082.9,139.400,943.8,3.88,7.4,255.992,3.17,0.71
|
||||||
|
1992,2,8244.294,5466.7,988.912,777.762,6129.5,140.500,963.2,3.50,7.6,256.894,3.14,0.36
|
||||||
|
1992,3,8329.361,5527.1,999.135,786.639,6160.6,141.700,1003.8,2.97,7.6,257.861,3.4,-0.44
|
||||||
|
1992,4,8417.016,5594.6,1030.758,787.064,6248.2,142.800,1030.4,3.12,7.4,258.679,3.09,0.02
|
||||||
|
1993,1,8432.485,5617.2,1054.979,762.901,6156.5,143.800,1047.6,2.92,7.2,259.414,2.79,0.13
|
||||||
|
1993,2,8486.435,5671.1,1063.263,752.158,6252.3,144.500,1084.5,3.02,7.1,260.255,1.94,1.08
|
||||||
|
1993,3,8531.108,5732.7,1062.514,744.227,6265.7,145.600,1113,3.00,6.8,261.163,3.03,-0.04
|
||||||
|
1993,4,8643.769,5783.7,1118.583,748.102,6358.1,146.300,1131.6,3.05,6.6,261.919,1.92,1.13
|
||||||
|
1994,1,8727.919,5848.1,1166.845,721.288,6332.6,147.200,1141.1,3.48,6.6,262.631,2.45,1.02
|
||||||
|
1994,2,8847.303,5891.5,1234.855,717.197,6440.6,148.400,1150.5,4.20,6.2,263.436,3.25,0.96
|
||||||
|
1994,3,8904.289,5938.7,1212.655,736.890,6487.9,149.400,1150.1,4.68,6,264.301,2.69,2
|
||||||
|
1994,4,9003.180,5997.3,1269.190,716.702,6574.0,150.500,1151.4,5.53,5.6,265.044,2.93,2.6
|
||||||
|
1995,1,9025.267,6004.3,1282.090,715.326,6616.6,151.800,1149.3,5.72,5.5,265.755,3.44,2.28
|
||||||
|
1995,2,9044.668,6053.5,1247.610,712.492,6617.2,152.600,1145.4,5.52,5.7,266.557,2.1,3.42
|
||||||
|
1995,3,9120.684,6107.6,1235.601,707.649,6666.8,153.500,1137.3,5.32,5.7,267.456,2.35,2.97
|
||||||
|
1995,4,9184.275,6150.6,1270.392,681.081,6706.2,154.700,1123.5,5.17,5.6,268.151,3.11,2.05
|
||||||
|
1996,1,9247.188,6206.9,1287.128,695.265,6777.7,156.100,1124.8,4.91,5.5,268.853,3.6,1.31
|
||||||
|
1996,2,9407.052,6277.1,1353.795,705.172,6850.6,157.000,1112.4,5.09,5.5,269.667,2.3,2.79
|
||||||
|
1996,3,9488.879,6314.6,1422.059,692.741,6908.9,158.200,1086.1,5.04,5.3,270.581,3.05,2
|
||||||
|
1996,4,9592.458,6366.1,1418.193,690.744,6946.8,159.400,1081.5,4.99,5.3,271.360,3.02,1.97
|
||||||
|
1997,1,9666.235,6430.2,1451.304,681.445,7008.9,159.900,1063.8,5.10,5.2,272.083,1.25,3.85
|
||||||
|
1997,2,9809.551,6456.2,1543.976,693.525,7061.5,160.400,1066.2,5.01,5,272.912,1.25,3.76
|
||||||
|
1997,3,9932.672,6566.0,1571.426,691.261,7142.4,161.500,1065.5,5.02,4.9,273.852,2.73,2.29
|
||||||
|
1997,4,10008.874,6641.1,1596.523,690.311,7241.5,162.000,1074.4,5.11,4.7,274.626,1.24,3.88
|
||||||
|
1998,1,10103.425,6707.2,1672.732,668.783,7406.2,162.200,1076.1,5.02,4.6,275.304,0.49,4.53
|
||||||
|
1998,2,10194.277,6822.6,1652.716,687.184,7512.0,163.200,1075,4.98,4.4,276.115,2.46,2.52
|
||||||
|
1998,3,10328.787,6913.1,1700.071,681.472,7591.0,163.900,1086,4.49,4.5,277.003,1.71,2.78
|
||||||
|
1998,4,10507.575,7019.1,1754.743,688.147,7646.5,164.700,1097.8,4.38,4.4,277.790,1.95,2.43
|
||||||
|
1999,1,10601.179,7088.3,1809.993,683.601,7698.4,165.900,1101.9,4.39,4.3,278.451,2.9,1.49
|
||||||
|
1999,2,10684.049,7199.9,1803.674,683.594,7716.0,166.700,1098.7,4.54,4.3,279.295,1.92,2.62
|
||||||
|
1999,3,10819.914,7286.4,1848.949,697.936,7765.9,168.100,1102.3,4.75,4.2,280.203,3.35,1.41
|
||||||
|
1999,4,11014.254,7389.2,1914.567,713.445,7887.7,169.300,1121.9,5.20,4.1,280.976,2.85,2.35
|
||||||
|
2000,1,11043.044,7501.3,1887.836,685.216,8053.4,170.900,1113.5,5.63,4,281.653,3.76,1.87
|
||||||
|
2000,2,11258.454,7571.8,2018.529,712.641,8135.9,172.700,1103,5.81,3.9,282.385,4.19,1.62
|
||||||
|
2000,3,11267.867,7645.9,1986.956,698.827,8222.3,173.900,1098.7,6.07,4,283.190,2.77,3.3
|
||||||
|
2000,4,11334.544,7713.5,1987.845,695.597,8234.6,175.600,1097.7,5.70,3.9,283.900,3.89,1.81
|
||||||
|
2001,1,11297.171,7744.3,1882.691,710.403,8296.5,176.400,1114.9,4.39,4.2,284.550,1.82,2.57
|
||||||
|
2001,2,11371.251,7773.5,1876.650,725.623,8273.7,177.400,1139.7,3.54,4.4,285.267,2.26,1.28
|
||||||
|
2001,3,11340.075,7807.7,1837.074,730.493,8484.5,177.600,1166,2.72,4.8,286.047,0.45,2.27
|
||||||
|
2001,4,11380.128,7930.0,1731.189,739.318,8385.5,177.700,1190.9,1.74,5.5,286.728,0.23,1.51
|
||||||
|
2002,1,11477.868,7957.3,1789.327,756.915,8611.6,179.300,1185.9,1.75,5.7,287.328,3.59,-1.84
|
||||||
|
2002,2,11538.770,7997.8,1810.779,774.408,8658.9,180.000,1199.5,1.70,5.8,288.028,1.56,0.14
|
||||||
|
2002,3,11596.430,8052.0,1814.531,786.673,8629.2,181.200,1204,1.61,5.7,288.783,2.66,-1.05
|
||||||
|
2002,4,11598.824,8080.6,1813.219,799.967,8649.6,182.600,1226.8,1.20,5.8,289.421,3.08,-1.88
|
||||||
|
2003,1,11645.819,8122.3,1813.141,800.196,8681.3,183.200,1248.4,1.14,5.9,290.019,1.31,-0.17
|
||||||
|
2003,2,11738.706,8197.8,1823.698,838.775,8812.5,183.700,1287.9,0.96,6.2,290.704,1.09,-0.13
|
||||||
|
2003,3,11935.461,8312.1,1889.883,839.598,8935.4,184.900,1297.3,0.94,6.1,291.449,2.6,-1.67
|
||||||
|
2003,4,12042.817,8358.0,1959.783,845.722,8986.4,186.300,1306.1,0.90,5.8,292.057,3.02,-2.11
|
||||||
|
2004,1,12127.623,8437.6,1970.015,856.570,9025.9,187.400,1332.1,0.94,5.7,292.635,2.35,-1.42
|
||||||
|
2004,2,12213.818,8483.2,2055.580,861.440,9115.0,189.100,1340.5,1.21,5.6,293.310,3.61,-2.41
|
||||||
|
2004,3,12303.533,8555.8,2082.231,876.385,9175.9,190.800,1361,1.63,5.4,294.066,3.58,-1.95
|
||||||
|
2004,4,12410.282,8654.2,2125.152,865.596,9303.4,191.800,1366.6,2.20,5.4,294.741,2.09,0.11
|
||||||
|
2005,1,12534.113,8719.0,2170.299,869.204,9189.6,193.800,1357.8,2.69,5.3,295.308,4.15,-1.46
|
||||||
|
2005,2,12587.535,8802.9,2131.468,870.044,9253.0,194.700,1366.6,3.01,5.1,295.994,1.85,1.16
|
||||||
|
2005,3,12683.153,8865.6,2154.949,890.394,9308.0,199.200,1375,3.52,5,296.770,9.14,-5.62
|
||||||
|
2005,4,12748.699,8888.5,2232.193,875.557,9358.7,199.400,1380.6,4.00,4.9,297.435,0.4,3.6
|
||||||
|
2006,1,12915.938,8986.6,2264.721,900.511,9533.8,200.700,1380.5,4.51,4.7,298.061,2.6,1.91
|
||||||
|
2006,2,12962.462,9035.0,2261.247,892.839,9617.3,202.700,1369.2,4.82,4.7,298.766,3.97,0.85
|
||||||
|
2006,3,12965.916,9090.7,2229.636,892.002,9662.5,201.900,1369.4,4.90,4.7,299.593,-1.58,6.48
|
||||||
|
2006,4,13060.679,9181.6,2165.966,894.404,9788.8,203.574,1373.6,4.92,4.4,300.320,3.3,1.62
|
||||||
|
2007,1,13099.901,9265.1,2132.609,882.766,9830.2,205.920,1379.7,4.95,4.5,300.977,4.58,0.36
|
||||||
|
2007,2,13203.977,9291.5,2162.214,898.713,9842.7,207.338,1370,4.72,4.5,301.714,2.75,1.97
|
||||||
|
2007,3,13321.109,9335.6,2166.491,918.983,9883.9,209.133,1379.2,4.00,4.7,302.509,3.45,0.55
|
||||||
|
2007,4,13391.249,9363.6,2123.426,925.110,9886.2,212.495,1377.4,3.01,4.8,303.204,6.38,-3.37
|
||||||
|
2008,1,13366.865,9349.6,2082.886,943.372,9826.8,213.997,1384,1.56,4.9,303.803,2.82,-1.26
|
||||||
|
2008,2,13415.266,9351.0,2026.518,961.280,10059.0,218.610,1409.3,1.74,5.4,304.483,8.53,-6.79
|
||||||
|
2008,3,13324.600,9267.7,1990.693,991.551,9838.3,216.889,1474.7,1.17,6,305.270,-3.16,4.33
|
||||||
|
2008,4,13141.920,9195.3,1857.661,1007.273,9920.4,212.174,1576.5,0.12,6.9,305.952,-8.79,8.91
|
||||||
|
2009,1,12925.410,9209.2,1558.494,996.287,9926.4,212.671,1592.8,0.22,8.1,306.547,0.94,-0.71
|
||||||
|
2009,2,12901.504,9189.0,1456.678,1023.528,10077.5,214.469,1653.6,0.18,9.2,307.226,3.37,-3.19
|
||||||
|
2009,3,12990.341,9256.0,1486.398,1044.088,10040.6,216.385,1673.9,0.12,9.6,308.013,3.56,-3.44
|
||||||
|
Binary file not shown.
Binary file not shown.
@ -0,0 +1,214 @@
|
|||||||
|
Series Id: LNS14000000Q
|
||||||
|
|
||||||
|
Seasonally Adjusted
|
||||||
|
Series title: (Seas) Unemployment Rate
|
||||||
|
Labor force status: Unemployment rate
|
||||||
|
Type of data: Percent or rate
|
||||||
|
Age: 16 years and over
|
||||||
|
|
||||||
|
|
||||||
|
Series id,Year,Period,Value,
|
||||||
|
LNS14000000Q,1959,Q01,5.8
|
||||||
|
LNS14000000Q,1959,Q02,5.1
|
||||||
|
LNS14000000Q,1959,Q03,5.3
|
||||||
|
LNS14000000Q,1959,Q04,5.6
|
||||||
|
LNS14000000Q,1960,Q01,5.2
|
||||||
|
LNS14000000Q,1960,Q02,5.2
|
||||||
|
LNS14000000Q,1960,Q03,5.6
|
||||||
|
LNS14000000Q,1960,Q04,6.3
|
||||||
|
LNS14000000Q,1961,Q01,6.8
|
||||||
|
LNS14000000Q,1961,Q02,7.0
|
||||||
|
LNS14000000Q,1961,Q03,6.8
|
||||||
|
LNS14000000Q,1961,Q04,6.2
|
||||||
|
LNS14000000Q,1962,Q01,5.6
|
||||||
|
LNS14000000Q,1962,Q02,5.5
|
||||||
|
LNS14000000Q,1962,Q03,5.6
|
||||||
|
LNS14000000Q,1962,Q04,5.5
|
||||||
|
LNS14000000Q,1963,Q01,5.8
|
||||||
|
LNS14000000Q,1963,Q02,5.7
|
||||||
|
LNS14000000Q,1963,Q03,5.5
|
||||||
|
LNS14000000Q,1963,Q04,5.6
|
||||||
|
LNS14000000Q,1964,Q01,5.5
|
||||||
|
LNS14000000Q,1964,Q02,5.2
|
||||||
|
LNS14000000Q,1964,Q03,5.0
|
||||||
|
LNS14000000Q,1964,Q04,5.0
|
||||||
|
LNS14000000Q,1965,Q01,4.9
|
||||||
|
LNS14000000Q,1965,Q02,4.7
|
||||||
|
LNS14000000Q,1965,Q03,4.4
|
||||||
|
LNS14000000Q,1965,Q04,4.1
|
||||||
|
LNS14000000Q,1966,Q01,3.9
|
||||||
|
LNS14000000Q,1966,Q02,3.8
|
||||||
|
LNS14000000Q,1966,Q03,3.8
|
||||||
|
LNS14000000Q,1966,Q04,3.7
|
||||||
|
LNS14000000Q,1967,Q01,3.8
|
||||||
|
LNS14000000Q,1967,Q02,3.8
|
||||||
|
LNS14000000Q,1967,Q03,3.8
|
||||||
|
LNS14000000Q,1967,Q04,3.9
|
||||||
|
LNS14000000Q,1968,Q01,3.7
|
||||||
|
LNS14000000Q,1968,Q02,3.5
|
||||||
|
LNS14000000Q,1968,Q03,3.5
|
||||||
|
LNS14000000Q,1968,Q04,3.4
|
||||||
|
LNS14000000Q,1969,Q01,3.4
|
||||||
|
LNS14000000Q,1969,Q02,3.4
|
||||||
|
LNS14000000Q,1969,Q03,3.6
|
||||||
|
LNS14000000Q,1969,Q04,3.6
|
||||||
|
LNS14000000Q,1970,Q01,4.2
|
||||||
|
LNS14000000Q,1970,Q02,4.8
|
||||||
|
LNS14000000Q,1970,Q03,5.2
|
||||||
|
LNS14000000Q,1970,Q04,5.8
|
||||||
|
LNS14000000Q,1971,Q01,5.9
|
||||||
|
LNS14000000Q,1971,Q02,5.9
|
||||||
|
LNS14000000Q,1971,Q03,6.0
|
||||||
|
LNS14000000Q,1971,Q04,6.0
|
||||||
|
LNS14000000Q,1972,Q01,5.8
|
||||||
|
LNS14000000Q,1972,Q02,5.7
|
||||||
|
LNS14000000Q,1972,Q03,5.6
|
||||||
|
LNS14000000Q,1972,Q04,5.3
|
||||||
|
LNS14000000Q,1973,Q01,5.0
|
||||||
|
LNS14000000Q,1973,Q02,4.9
|
||||||
|
LNS14000000Q,1973,Q03,4.8
|
||||||
|
LNS14000000Q,1973,Q04,4.8
|
||||||
|
LNS14000000Q,1974,Q01,5.1
|
||||||
|
LNS14000000Q,1974,Q02,5.2
|
||||||
|
LNS14000000Q,1974,Q03,5.6
|
||||||
|
LNS14000000Q,1974,Q04,6.6
|
||||||
|
LNS14000000Q,1975,Q01,8.2
|
||||||
|
LNS14000000Q,1975,Q02,8.9
|
||||||
|
LNS14000000Q,1975,Q03,8.5
|
||||||
|
LNS14000000Q,1975,Q04,8.3
|
||||||
|
LNS14000000Q,1976,Q01,7.7
|
||||||
|
LNS14000000Q,1976,Q02,7.6
|
||||||
|
LNS14000000Q,1976,Q03,7.7
|
||||||
|
LNS14000000Q,1976,Q04,7.8
|
||||||
|
LNS14000000Q,1977,Q01,7.5
|
||||||
|
LNS14000000Q,1977,Q02,7.1
|
||||||
|
LNS14000000Q,1977,Q03,6.9
|
||||||
|
LNS14000000Q,1977,Q04,6.6
|
||||||
|
LNS14000000Q,1978,Q01,6.3
|
||||||
|
LNS14000000Q,1978,Q02,6.0
|
||||||
|
LNS14000000Q,1978,Q03,6.0
|
||||||
|
LNS14000000Q,1978,Q04,5.9
|
||||||
|
LNS14000000Q,1979,Q01,5.9
|
||||||
|
LNS14000000Q,1979,Q02,5.7
|
||||||
|
LNS14000000Q,1979,Q03,5.9
|
||||||
|
LNS14000000Q,1979,Q04,5.9
|
||||||
|
LNS14000000Q,1980,Q01,6.3
|
||||||
|
LNS14000000Q,1980,Q02,7.3
|
||||||
|
LNS14000000Q,1980,Q03,7.7
|
||||||
|
LNS14000000Q,1980,Q04,7.4
|
||||||
|
LNS14000000Q,1981,Q01,7.4
|
||||||
|
LNS14000000Q,1981,Q02,7.4
|
||||||
|
LNS14000000Q,1981,Q03,7.4
|
||||||
|
LNS14000000Q,1981,Q04,8.2
|
||||||
|
LNS14000000Q,1982,Q01,8.8
|
||||||
|
LNS14000000Q,1982,Q02,9.4
|
||||||
|
LNS14000000Q,1982,Q03,9.9
|
||||||
|
LNS14000000Q,1982,Q04,10.7
|
||||||
|
LNS14000000Q,1983,Q01,10.4
|
||||||
|
LNS14000000Q,1983,Q02,10.1
|
||||||
|
LNS14000000Q,1983,Q03,9.4
|
||||||
|
LNS14000000Q,1983,Q04,8.5
|
||||||
|
LNS14000000Q,1984,Q01,7.9
|
||||||
|
LNS14000000Q,1984,Q02,7.5
|
||||||
|
LNS14000000Q,1984,Q03,7.4
|
||||||
|
LNS14000000Q,1984,Q04,7.3
|
||||||
|
LNS14000000Q,1985,Q01,7.3
|
||||||
|
LNS14000000Q,1985,Q02,7.3
|
||||||
|
LNS14000000Q,1985,Q03,7.2
|
||||||
|
LNS14000000Q,1985,Q04,7.0
|
||||||
|
LNS14000000Q,1986,Q01,7.0
|
||||||
|
LNS14000000Q,1986,Q02,7.2
|
||||||
|
LNS14000000Q,1986,Q03,7.0
|
||||||
|
LNS14000000Q,1986,Q04,6.8
|
||||||
|
LNS14000000Q,1987,Q01,6.6
|
||||||
|
LNS14000000Q,1987,Q02,6.3
|
||||||
|
LNS14000000Q,1987,Q03,6.0
|
||||||
|
LNS14000000Q,1987,Q04,5.9
|
||||||
|
LNS14000000Q,1988,Q01,5.7
|
||||||
|
LNS14000000Q,1988,Q02,5.5
|
||||||
|
LNS14000000Q,1988,Q03,5.5
|
||||||
|
LNS14000000Q,1988,Q04,5.3
|
||||||
|
LNS14000000Q,1989,Q01,5.2
|
||||||
|
LNS14000000Q,1989,Q02,5.2
|
||||||
|
LNS14000000Q,1989,Q03,5.3
|
||||||
|
LNS14000000Q,1989,Q04,5.4
|
||||||
|
LNS14000000Q,1990,Q01,5.3
|
||||||
|
LNS14000000Q,1990,Q02,5.3
|
||||||
|
LNS14000000Q,1990,Q03,5.7
|
||||||
|
LNS14000000Q,1990,Q04,6.1
|
||||||
|
LNS14000000Q,1991,Q01,6.6
|
||||||
|
LNS14000000Q,1991,Q02,6.8
|
||||||
|
LNS14000000Q,1991,Q03,6.9
|
||||||
|
LNS14000000Q,1991,Q04,7.1
|
||||||
|
LNS14000000Q,1992,Q01,7.4
|
||||||
|
LNS14000000Q,1992,Q02,7.6
|
||||||
|
LNS14000000Q,1992,Q03,7.6
|
||||||
|
LNS14000000Q,1992,Q04,7.4
|
||||||
|
LNS14000000Q,1993,Q01,7.2
|
||||||
|
LNS14000000Q,1993,Q02,7.1
|
||||||
|
LNS14000000Q,1993,Q03,6.8
|
||||||
|
LNS14000000Q,1993,Q04,6.6
|
||||||
|
LNS14000000Q,1994,Q01,6.6
|
||||||
|
LNS14000000Q,1994,Q02,6.2
|
||||||
|
LNS14000000Q,1994,Q03,6.0
|
||||||
|
LNS14000000Q,1994,Q04,5.6
|
||||||
|
LNS14000000Q,1995,Q01,5.5
|
||||||
|
LNS14000000Q,1995,Q02,5.7
|
||||||
|
LNS14000000Q,1995,Q03,5.7
|
||||||
|
LNS14000000Q,1995,Q04,5.6
|
||||||
|
LNS14000000Q,1996,Q01,5.5
|
||||||
|
LNS14000000Q,1996,Q02,5.5
|
||||||
|
LNS14000000Q,1996,Q03,5.3
|
||||||
|
LNS14000000Q,1996,Q04,5.3
|
||||||
|
LNS14000000Q,1997,Q01,5.2
|
||||||
|
LNS14000000Q,1997,Q02,5.0
|
||||||
|
LNS14000000Q,1997,Q03,4.9
|
||||||
|
LNS14000000Q,1997,Q04,4.7
|
||||||
|
LNS14000000Q,1998,Q01,4.6
|
||||||
|
LNS14000000Q,1998,Q02,4.4
|
||||||
|
LNS14000000Q,1998,Q03,4.5
|
||||||
|
LNS14000000Q,1998,Q04,4.4
|
||||||
|
LNS14000000Q,1999,Q01,4.3
|
||||||
|
LNS14000000Q,1999,Q02,4.3
|
||||||
|
LNS14000000Q,1999,Q03,4.2
|
||||||
|
LNS14000000Q,1999,Q04,4.1
|
||||||
|
LNS14000000Q,2000,Q01,4.0
|
||||||
|
LNS14000000Q,2000,Q02,3.9
|
||||||
|
LNS14000000Q,2000,Q03,4.0
|
||||||
|
LNS14000000Q,2000,Q04,3.9
|
||||||
|
LNS14000000Q,2001,Q01,4.2
|
||||||
|
LNS14000000Q,2001,Q02,4.4
|
||||||
|
LNS14000000Q,2001,Q03,4.8
|
||||||
|
LNS14000000Q,2001,Q04,5.5
|
||||||
|
LNS14000000Q,2002,Q01,5.7
|
||||||
|
LNS14000000Q,2002,Q02,5.8
|
||||||
|
LNS14000000Q,2002,Q03,5.7
|
||||||
|
LNS14000000Q,2002,Q04,5.8
|
||||||
|
LNS14000000Q,2003,Q01,5.9
|
||||||
|
LNS14000000Q,2003,Q02,6.2
|
||||||
|
LNS14000000Q,2003,Q03,6.1
|
||||||
|
LNS14000000Q,2003,Q04,5.8
|
||||||
|
LNS14000000Q,2004,Q01,5.7
|
||||||
|
LNS14000000Q,2004,Q02,5.6
|
||||||
|
LNS14000000Q,2004,Q03,5.4
|
||||||
|
LNS14000000Q,2004,Q04,5.4
|
||||||
|
LNS14000000Q,2005,Q01,5.3
|
||||||
|
LNS14000000Q,2005,Q02,5.1
|
||||||
|
LNS14000000Q,2005,Q03,5.0
|
||||||
|
LNS14000000Q,2005,Q04,4.9
|
||||||
|
LNS14000000Q,2006,Q01,4.7
|
||||||
|
LNS14000000Q,2006,Q02,4.7
|
||||||
|
LNS14000000Q,2006,Q03,4.7
|
||||||
|
LNS14000000Q,2006,Q04,4.4
|
||||||
|
LNS14000000Q,2007,Q01,4.5
|
||||||
|
LNS14000000Q,2007,Q02,4.5
|
||||||
|
LNS14000000Q,2007,Q03,4.7
|
||||||
|
LNS14000000Q,2007,Q04,4.8
|
||||||
|
LNS14000000Q,2008,Q01,4.9
|
||||||
|
LNS14000000Q,2008,Q02,5.4
|
||||||
|
LNS14000000Q,2008,Q03,6.0
|
||||||
|
LNS14000000Q,2008,Q04,6.9
|
||||||
|
LNS14000000Q,2009,Q01,8.1
|
||||||
|
LNS14000000Q,2009,Q02,9.2
|
||||||
|
LNS14000000Q,2009,Q03,9.6
|
||||||
|
|
||||||
|
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,61 @@
|
|||||||
|
"""Name of dataset."""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """E.g., This is public domain."""
|
||||||
|
TITLE = """Title of the dataset"""
|
||||||
|
SOURCE = """
|
||||||
|
This section should provide a link to the original dataset if possible and
|
||||||
|
attribution and correspondance information for the dataset's original author
|
||||||
|
if so desired.
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """A short description."""
|
||||||
|
|
||||||
|
DESCRLONG = """A longer description of the dataset."""
|
||||||
|
|
||||||
|
#suggested notes
|
||||||
|
NOTE = """
|
||||||
|
Number of observations:
|
||||||
|
Number of variables:
|
||||||
|
Variable name definitions:
|
||||||
|
|
||||||
|
Any other useful information that does not fit into the above categories.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
from pandas import Series, DataFrame
|
||||||
|
|
||||||
|
from scikits.statsmodels.tools import Dataset
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Load the Nile data and return a Dataset class instance.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
names = list(data.dtype.names)
|
||||||
|
endog_name = 'volume'
|
||||||
|
endog = array(data[endog_name], dtype=float)
|
||||||
|
dataset = Dataset(data=data, names=[endog_name], endog=endog,
|
||||||
|
endog_name=endog_name)
|
||||||
|
return dataset
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
data = DataFrame(_get_data())
|
||||||
|
# TODO: time series
|
||||||
|
endog = Series(data['volume'], index=data['year'].astype(int))
|
||||||
|
dataset = Dataset(data=data, names=list(data.columns),
|
||||||
|
endog=endog, endog_name='volume')
|
||||||
|
return dataset
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(filepath + '/nile.csv', 'rb'), delimiter=",",
|
||||||
|
names=True, dtype=float)
|
||||||
|
return data
|
||||||
@ -0,0 +1,101 @@
|
|||||||
|
year,volume
|
||||||
|
1871,1120
|
||||||
|
1872,1160
|
||||||
|
1873,963
|
||||||
|
1874,1210
|
||||||
|
1875,1160
|
||||||
|
1876,1160
|
||||||
|
1877,813
|
||||||
|
1878,1230
|
||||||
|
1879,1370
|
||||||
|
1880,1140
|
||||||
|
1881,995
|
||||||
|
1882,935
|
||||||
|
1883,1110
|
||||||
|
1884,994
|
||||||
|
1885,1020
|
||||||
|
1886,960
|
||||||
|
1887,1180
|
||||||
|
1888,799
|
||||||
|
1889,958
|
||||||
|
1890,1140
|
||||||
|
1891,1100
|
||||||
|
1892,1210
|
||||||
|
1893,1150
|
||||||
|
1894,1250
|
||||||
|
1895,1260
|
||||||
|
1896,1220
|
||||||
|
1897,1030
|
||||||
|
1898,1100
|
||||||
|
1899,774
|
||||||
|
1900,840
|
||||||
|
1901,874
|
||||||
|
1902,694
|
||||||
|
1903,940
|
||||||
|
1904,833
|
||||||
|
1905,701
|
||||||
|
1906,916
|
||||||
|
1907,692
|
||||||
|
1908,1020
|
||||||
|
1909,1050
|
||||||
|
1910,969
|
||||||
|
1911,831
|
||||||
|
1912,726
|
||||||
|
1913,456
|
||||||
|
1914,824
|
||||||
|
1915,702
|
||||||
|
1916,1120
|
||||||
|
1917,1100
|
||||||
|
1918,832
|
||||||
|
1919,764
|
||||||
|
1920,821
|
||||||
|
1921,768
|
||||||
|
1922,845
|
||||||
|
1923,864
|
||||||
|
1924,862
|
||||||
|
1925,698
|
||||||
|
1926,845
|
||||||
|
1927,744
|
||||||
|
1928,796
|
||||||
|
1929,1040
|
||||||
|
1930,759
|
||||||
|
1931,781
|
||||||
|
1932,865
|
||||||
|
1933,845
|
||||||
|
1934,944
|
||||||
|
1935,984
|
||||||
|
1936,897
|
||||||
|
1937,822
|
||||||
|
1938,1010
|
||||||
|
1939,771
|
||||||
|
1940,676
|
||||||
|
1941,649
|
||||||
|
1942,846
|
||||||
|
1943,812
|
||||||
|
1944,742
|
||||||
|
1945,801
|
||||||
|
1946,1040
|
||||||
|
1947,860
|
||||||
|
1948,874
|
||||||
|
1949,848
|
||||||
|
1950,890
|
||||||
|
1951,744
|
||||||
|
1952,749
|
||||||
|
1953,838
|
||||||
|
1954,1050
|
||||||
|
1955,918
|
||||||
|
1956,986
|
||||||
|
1957,797
|
||||||
|
1958,923
|
||||||
|
1959,975
|
||||||
|
1960,815
|
||||||
|
1961,1020
|
||||||
|
1962,906
|
||||||
|
1963,901
|
||||||
|
1964,1170
|
||||||
|
1965,912
|
||||||
|
1966,746
|
||||||
|
1967,919
|
||||||
|
1968,718
|
||||||
|
1969,714
|
||||||
|
1970,740
|
||||||
|
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,87 @@
|
|||||||
|
"""RAND Health Insurance Experiment Data"""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """This is in the public domain."""
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
The data was collected by the RAND corporation as part of the Health
|
||||||
|
Insurance Experiment (HIE).
|
||||||
|
|
||||||
|
http://www.rand.org/health/projects/hie/
|
||||||
|
|
||||||
|
This data was used in::
|
||||||
|
|
||||||
|
Cameron, A.C. amd Trivedi, P.K. 2005. `Microeconometrics: Methods
|
||||||
|
and Applications,` Cambridge: New York.
|
||||||
|
|
||||||
|
And was obtained from: <http://cameron.econ.ucdavis.edu/mmabook/mmadata.html>
|
||||||
|
|
||||||
|
See randhie/src for the original data and description. The data included
|
||||||
|
here contains only a subset of the original data. The data varies slightly
|
||||||
|
compared to that reported in Cameron and Trivedi.
|
||||||
|
"""
|
||||||
|
|
||||||
|
DESCRSHORT = """The RAND Co. Health Insurance Experiment Data"""
|
||||||
|
|
||||||
|
DESCRLONG = """"""
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of observations - 20,190
|
||||||
|
Number of variables - 10
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
mdvis - Number of outpatient visits to an MD
|
||||||
|
lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100
|
||||||
|
idp - 1 if individual deductible plan, 0 otherwise
|
||||||
|
lpi - ln(max(1, annual participation incentive payment))
|
||||||
|
fmde - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise
|
||||||
|
physlm - 1 if the person has a physical limitation
|
||||||
|
disea - number of chronic diseases
|
||||||
|
hlthg - 1 if self-rated health is good
|
||||||
|
hlthf - 1 if self-rated health is fair
|
||||||
|
hlthp - 1 if self-rated health is poor
|
||||||
|
(Omitted category is excellent self-rated health)
|
||||||
|
"""
|
||||||
|
|
||||||
|
from numpy import recfromtxt, column_stack, array
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
PATH = '%s/%s' % (dirname(abspath(__file__)), 'randhie.csv')
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Loads the RAND HIE data and returns a Dataset class.
|
||||||
|
|
||||||
|
----------
|
||||||
|
endog - response variable, mdvis
|
||||||
|
exog - design
|
||||||
|
|
||||||
|
Returns
|
||||||
|
Load instance:
|
||||||
|
a class of the data with array attrbutes 'endog' and 'exog'
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""
|
||||||
|
Loads the RAND HIE data and returns a Dataset class.
|
||||||
|
|
||||||
|
----------
|
||||||
|
endog - response variable, mdvis
|
||||||
|
exog - design
|
||||||
|
|
||||||
|
Returns
|
||||||
|
Load instance:
|
||||||
|
a class of the data with array attrbutes 'endog' and 'exog'
|
||||||
|
"""
|
||||||
|
from pandas import read_csv
|
||||||
|
data = read_csv(PATH)
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=0)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = recfromtxt(open(PATH, "rb"), delimiter=",", names=True, dtype=float)
|
||||||
|
return data
|
||||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,49 @@
|
|||||||
|
storage display value
|
||||||
|
variable name type format label variable label
|
||||||
|
--------------------------------------------------------------------
|
||||||
|
plan float %9.0g hie plan number
|
||||||
|
site float %9.0g site
|
||||||
|
coins float %9.0g coinsurance -- medical
|
||||||
|
tookphys float %9.0g took baseline physical
|
||||||
|
year float %9.0g study year
|
||||||
|
zper float %9.0g person id, leading digit is sit
|
||||||
|
black float %9.0g black
|
||||||
|
income float %9.0g income based on annual income r
|
||||||
|
xage float %9.0g age that year
|
||||||
|
female float %9.0g female
|
||||||
|
educdec float %9.0g education of decision maker
|
||||||
|
time float %9.0g time eligible during the year
|
||||||
|
outpdol float %9.0g outpatient exp. excl. ment and
|
||||||
|
drugdol float %9.0g drugs purchased, outpatient
|
||||||
|
suppdol float %9.0g supplies purchased, outpatient
|
||||||
|
mentdol float %9.0g psychotherapy exp., outpatient
|
||||||
|
inpdol float %9.0g inpatient exp., facilities & md
|
||||||
|
meddol float %9.0g medical exp excl outpatient men
|
||||||
|
totadm float %9.0g number of hosp. admissions
|
||||||
|
inpmis float %9.0g missing any inpatient charges
|
||||||
|
mentvis float %9.0g number psychotherapy visits
|
||||||
|
mdvis float %9.0g number face-to-fact md visits
|
||||||
|
notmdvis float %9.0g number face-to-face, not-md vis
|
||||||
|
num float %9.0g family size
|
||||||
|
mhi float %9.0g mental health index -- baselin
|
||||||
|
disea float %9.0g count of chronic diseases -- ba
|
||||||
|
physlm float %9.0g physical limitations -- baselin
|
||||||
|
ghindx float %9.0g general health index -- baselin
|
||||||
|
mdeoff float %9.0g maximum expenditure offer
|
||||||
|
pioff float %9.0g participation incentive
|
||||||
|
child float %9.0g child
|
||||||
|
fchild float %9.0g female child
|
||||||
|
lfam float %9.0g log of family size
|
||||||
|
lpi float %9.0g log participation incentive
|
||||||
|
idp float %9.0g individual deductible plan
|
||||||
|
logc float %9.0g log(coinsurance+1)
|
||||||
|
fmde float %9.0g function of mdeoff
|
||||||
|
hlthg float %9.0g good health
|
||||||
|
hlthf float %9.0g fair health
|
||||||
|
hlthp float %9.0g poor health
|
||||||
|
xghindx float %9.0g ghi with imputation
|
||||||
|
linc float %9.0g
|
||||||
|
lnum float %9.0g
|
||||||
|
lnmeddol float %9.0g
|
||||||
|
binexp float %9.0g
|
||||||
|
|
||||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,17 @@
|
|||||||
|
### SETUP ###
|
||||||
|
d <- read.table("./scotvote.csv",sep=",", header=T)
|
||||||
|
attach(d)
|
||||||
|
|
||||||
|
### MODEL ###
|
||||||
|
m1 <- glm(YES ~ COUTAX * UNEMPF + MOR + ACT + GDP + AGE,
|
||||||
|
family=Gamma)
|
||||||
|
results <- summary.glm(m1)
|
||||||
|
results
|
||||||
|
results['coefficients']
|
||||||
|
logLik(m1)
|
||||||
|
scale <- results$disp
|
||||||
|
Y <- YES
|
||||||
|
mu <- m1$fitted
|
||||||
|
llf <- -1/scale * sum(Y/mu+log(mu)+(scale-1)*log(Y)+log(scale)+scale*lgamma(1/scale))
|
||||||
|
print(llf)
|
||||||
|
print("This is the llf calculated with the formula")
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,84 @@
|
|||||||
|
"""Taxation Powers Vote for the Scottish Parliament 1997 dataset."""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """Used with express permission from the original author,
|
||||||
|
who retains all rights."""
|
||||||
|
TITLE = "Taxation Powers Vote for the Scottish Parliamant 1997"
|
||||||
|
SOURCE = """
|
||||||
|
Jeff Gill's `Generalized Linear Models: A Unified Approach`
|
||||||
|
|
||||||
|
http://jgill.wustl.edu/research/books.html
|
||||||
|
"""
|
||||||
|
DESCRSHORT = """Taxation Powers' Yes Vote for Scottish Parliamanet-1997"""
|
||||||
|
|
||||||
|
DESCRLONG = """
|
||||||
|
This data is based on the example in Gill and describes the proportion of
|
||||||
|
voters who voted Yes to grant the Scottish Parliament taxation powers.
|
||||||
|
The data are divided into 32 council districts. This example's explanatory
|
||||||
|
variables include the amount of council tax collected in pounds sterling as
|
||||||
|
of April 1997 per two adults before adjustments, the female percentage of
|
||||||
|
total claims for unemployment benefits as of January, 1998, the standardized
|
||||||
|
mortality rate (UK is 100), the percentage of labor force participation,
|
||||||
|
regional GDP, the percentage of children aged 5 to 15, and an interaction term
|
||||||
|
between female unemployment and the council tax.
|
||||||
|
|
||||||
|
The original source files and variable information are included in
|
||||||
|
/scotland/src/
|
||||||
|
"""
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of Observations - 32 (1 for each Scottish district)
|
||||||
|
|
||||||
|
Number of Variables - 8
|
||||||
|
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
YES - Proportion voting yes to granting taxation powers to the Scottish
|
||||||
|
parliament.
|
||||||
|
COUTAX - Amount of council tax collected in pounds steling as of April '97
|
||||||
|
UNEMPF - Female percentage of total unemployment benefits claims as of
|
||||||
|
January 1998
|
||||||
|
MOR - The standardized mortality rate (UK is 100)
|
||||||
|
ACT - Labor force participation (Short for active)
|
||||||
|
GDP - GDP per county
|
||||||
|
AGE - Percentage of children aged 5 to 15 in the county
|
||||||
|
COUTAX_FEMALEUNEMP - Interaction between COUTAX and UNEMPF
|
||||||
|
|
||||||
|
Council district names are included in the data file, though are not returned
|
||||||
|
by load.
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Load the Scotvote data and returns a Dataset instance.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""
|
||||||
|
Load the Scotvote data and returns a Dataset instance.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=0, dtype=float)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
data = np.recfromtxt(open(filepath + '/scotvote.csv',"rb"), delimiter=",",
|
||||||
|
names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))
|
||||||
|
return data
|
||||||
@ -0,0 +1,33 @@
|
|||||||
|
"COUNCILDIST","YES","COUTAX","UNEMPF","MOR","ACT","GDP","AGE","COUTAX_FEMALEUNEMP"
|
||||||
|
"Aberdeen_City",60.3,712,21,105,82.4,13566,12.3,14952
|
||||||
|
"Aberdeenshire",52.3,643,26.5,97,80.2,13566,15.3,17039.5
|
||||||
|
"Angus",53.4,679,28.3,113,86.3,9611,13.9,19215.7
|
||||||
|
"Argyll_and_Bute",57,801,27.1,109,80.4,9483,13.6,21707.1
|
||||||
|
"Clackmannanshire",68.7,753,22,115,64.7,9265,14.6,16566
|
||||||
|
"Dumfries_and_Galloway",48.8,714,24.3,107,79,9555,13.8,17350.2
|
||||||
|
"Dundee_City",65.5,920,21.2,118,72.2,9611,13.3,19504
|
||||||
|
"East_Ayrshire",70.5,779,20.5,114,75.2,9483,14.5,15969.5
|
||||||
|
"East_Dunbartonshire",59.1,771,23.2,102,81.1,9483,14.2,17887.2
|
||||||
|
"East_Lothian",62.7,724,20.5,112,80.3,12656,13.7,14842
|
||||||
|
"East_Renfrewshire",51.6,682,23.8,96,83,9483,14.6,16231.6
|
||||||
|
"Edinburgh_City",62,837,22.1,111,74.5,12656,11.6,18497.7
|
||||||
|
"Eilean_Siar_(Western_Isles)",68.4,599,19.9,117,83.8,8298,15.1,11920.1
|
||||||
|
"Falkirk",69.2,680,21.5,121,77.6,9265,13.7,14620
|
||||||
|
"Fife",64.7,747,22.5,109,77.9,8314,14.4,16807.5
|
||||||
|
"Glasgow_City",75,982,19.4,137,65.3,9483,13.3,19050.8
|
||||||
|
"Highland",62.1,719,25.9,109,80.9,8298,14.9,18622.1
|
||||||
|
"Inverclyde",67.2,831,18.5,138,80.2,9483,14.6,15373.5
|
||||||
|
"Midlothian",67.7,858,19.4,119,84.8,12656,14.3,16645.2
|
||||||
|
"Moray",52.7,652,27.2,108,86.4,13566,14.6,17734.4
|
||||||
|
"North_Ayrshire",65.7,718,23.7,115,73.5,9483,15,17016.6
|
||||||
|
"North_Lanarkshire",72.2,787,20.8,126,74.7,9483,14.9,16369.6
|
||||||
|
"Orkney_Islands",47.4,515,26.8,106,87.8,8298,15.3,13802
|
||||||
|
"Perth_and_Kinross",51.3,732,23,103,86.6,9611,13.8,16836
|
||||||
|
"Renfrewshire",63.6,783,20.5,125,78.5,9483,14.1,16051.5
|
||||||
|
"Scottish_Borders_The",50.7,612,23.7,100,80.6,9033,13.3,14504.4
|
||||||
|
"Shetland_Islands",51.6,486,23.2,117,84.8,8298,15.9,11275.2
|
||||||
|
"South_Ayrshire",56.2,765,23.6,105,79.2,9483,13.7,18054
|
||||||
|
"South_Lanarkshire",67.6,793,21.7,125,78.4,9483,14.5,17208.1
|
||||||
|
"Stirling",58.9,776,23,110,77.2,9265,13.6,17848
|
||||||
|
"West_Dunbartonshire",74.7,978,19.3,130,71.5,9483,15.3,18875.4
|
||||||
|
"West_Lothian",67.3,792,21.2,126,82.2,12656,15.1,16790.4
|
||||||
|
@ -0,0 +1,27 @@
|
|||||||
|
#########################################################################################################
|
||||||
|
# #
|
||||||
|
# This archive is part of the free distribution of data and statistical software code for #
|
||||||
|
# "Generalized Linear Models: A Unified Approach", Jeff Gill, Sage QASS Series. You are #
|
||||||
|
# free to use, modify, distribute, publish, etc. provided attribution. Please forward #
|
||||||
|
# bugs, complaints, comments, and useful changes to: jgill@latte.harvard.edu. #
|
||||||
|
# #
|
||||||
|
#########################################################################################################
|
||||||
|
|
||||||
|
Electoral Politics in Scotland. These data are from the 1997 vote that established a Scottish
|
||||||
|
Parliament with taxing powers. The data are culled from several different official UK documents
|
||||||
|
provided by the Office for National Statistics, the General Register Office for Scotland, the
|
||||||
|
Scottish Office: Education and Industry Department, the Scottish Department for Education
|
||||||
|
and Employment, The Scottish Office Office: Development Department, and David Boothroyd (thank you).
|
||||||
|
The files in this zip archive are:
|
||||||
|
|
||||||
|
scotland.readme this file
|
||||||
|
scotvote.dat the data file with a header indicating
|
||||||
|
|
||||||
|
scotland_births.html
|
||||||
|
scotland_changes.html
|
||||||
|
scotland_devolution.html
|
||||||
|
scotland_econ_summary.html
|
||||||
|
scotland_economics.html
|
||||||
|
scotland_education.html
|
||||||
|
scotland_housing.html
|
||||||
|
scotland_population.html these are html files with various details on the variables included.
|
||||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,364 @@
|
|||||||
|
<HTML>
|
||||||
|
<HEAD>
|
||||||
|
<TITLE>GENUKI: Administrative Areas of Scotland</TITLE>
|
||||||
|
</HEAD>
|
||||||
|
<BODY>
|
||||||
|
|
||||||
|
<P><A NAME="top"></A></P>
|
||||||
|
|
||||||
|
<A HREF="http://www.genuki.org.uk">
|
||||||
|
<IMG BORDER=0 ALIGN="LEFT" HSPACE=10 WIDTH=96 HEIGHT=78
|
||||||
|
SRC="/images/GenukiLogo.gif" ALT="GENUKI Home page"></A>
|
||||||
|
<TABLE BORDER="0" CELLSPACING="2" CELLPADDING="0">
|
||||||
|
<TR>
|
||||||
|
<TD>
|
||||||
|
<A HREF="index.html">
|
||||||
|
<IMG SRC="/images/u_arrow.gif" BORDER="0" WIDTH="30" HEIGHT="29"
|
||||||
|
ALIGN="BOTTOM"></A></TD>
|
||||||
|
<TD VALIGN="BOTTOM">
|
||||||
|
<A HREF="index.html">
|
||||||
|
Administrative Regions<BR>of the British Isles</A></TD>
|
||||||
|
<TD> </TD>
|
||||||
|
<TD><A HREF="http://www.genuki.org.uk/mindex.html">
|
||||||
|
<IMG SRC="/images/archiv.gif" BORDER="0" WIDTH="34" HEIGHT="32"
|
||||||
|
ALIGN="BOTTOM"></A></TD>
|
||||||
|
<TD VALIGN="BOTTOM"><A
|
||||||
|
HREF="http://www.genuki.org.uk/mindex.html">Contents</A></TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
<BR CLEAR="ALL">
|
||||||
|
|
||||||
|
<H3><CENTER>Administrative Areas of Scotland</CENTER></H3>
|
||||||
|
|
||||||
|
<P>The first table below
|
||||||
|
shows the historic counties and their administrative sub-divisions before
|
||||||
|
the first round of changes and lists the successor regions for each, that
|
||||||
|
is the post-change regions which contain some or all of the original county
|
||||||
|
area. The second table shows the regions after the first round of changes
|
||||||
|
and lists their successor unitary authorities. In all cases
|
||||||
|
only the top-tier authority is shown - either the top-tier in a two-tier
|
||||||
|
arrangement or a single tier authority (shown italicised).</P>
|
||||||
|
|
||||||
|
<P>The tables also show the Chapman County Codes (CCC) for each county and
|
||||||
|
region. These are unique 3 letter codes.</P>
|
||||||
|
|
||||||
|
<P>For a brief description of the administrative changes in the United Kingdom
|
||||||
|
see - <A HREF="UKchanges.html">Local Government Changes in the United
|
||||||
|
Kingdom</A>.</P>
|
||||||
|
|
||||||
|
<P>The following abbreviations are used in these tables:</P>
|
||||||
|
|
||||||
|
<P><CENTER><TABLE BORDER="1" CELLSPACING="2" CELLPADDING="0">
|
||||||
|
<TR>
|
||||||
|
<TH COLSPAN="2">Key</TH></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">(C)</TD>
|
||||||
|
<TD>County of a City</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">(U)</TD>
|
||||||
|
<TD>Unitary Authority</TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
</CENTER></P>
|
||||||
|
|
||||||
|
<P>Single-tier local authorities are shown italicised.</P>
|
||||||
|
|
||||||
|
<P>The links in the following table are to outline maps showing the location of each
|
||||||
|
county.</P>
|
||||||
|
|
||||||
|
<P><CENTER><TABLE BORDER="1" CELLSPACING="2" CELLPADDING="0">
|
||||||
|
<TR>
|
||||||
|
<TH COLSPAN="4">Scotland - changes of 1975</TH></TR>
|
||||||
|
<TR>
|
||||||
|
<TH>Historic County</TH>
|
||||||
|
<TH>CCC</TH>
|
||||||
|
<TH>Administration until 1975</TH>
|
||||||
|
<TH>Successor Regions</TH></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="ABD.html">Aberdeenshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">ABD</TD>
|
||||||
|
<TD VALIGN="TOP">Aberdeenshire<BR>
|
||||||
|
<I>Aberdeen (C)</I></TD>
|
||||||
|
<TD VALIGN="TOP">Grampian</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="ANS.html">Angus</A> <A HREF="#SN1">(1)</A></TD>
|
||||||
|
<TD VALIGN="TOP">ANS</TD>
|
||||||
|
<TD VALIGN="TOP">Angus <BR>
|
||||||
|
<I>Dundee (C)</I></TD>
|
||||||
|
<TD VALIGN="TOP">Tayside</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="ARL.html">Argyllshire</A> <A HREF="#SN2">(2)</A></TD>
|
||||||
|
<TD VALIGN="TOP">ARL</TD>
|
||||||
|
<TD VALIGN="TOP">Argyllshire</TD>
|
||||||
|
<TD VALIGN="TOP">Strathclyde<BR>
|
||||||
|
Highland</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="AYR.html">Ayrshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">AYR</TD>
|
||||||
|
<TD VALIGN="TOP">Ayrshire</TD>
|
||||||
|
<TD VALIGN="TOP">Strathclyde</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="BAN.html">Banffshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">BAN</TD>
|
||||||
|
<TD VALIGN="TOP">Banffshire</TD>
|
||||||
|
<TD VALIGN="TOP">Grampian</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="BEW.html">Berwickshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">BEW</TD>
|
||||||
|
<TD VALIGN="TOP">Berwickshire</TD>
|
||||||
|
<TD VALIGN="TOP">Borders</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="BUT.html">Bute</A> <A HREF="#SN3">(3)</A></TD>
|
||||||
|
<TD VALIGN="TOP">BUT</TD>
|
||||||
|
<TD VALIGN="TOP">Bute</TD>
|
||||||
|
<TD VALIGN="TOP">Strathclyde</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="CAI.html">Caithness</A></TD>
|
||||||
|
<TD VALIGN="TOP">CAI</TD>
|
||||||
|
<TD VALIGN="TOP">Caithness</TD>
|
||||||
|
<TD VALIGN="TOP">Highland</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="CLK.html">Clackmannanshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">CLK</TD>
|
||||||
|
<TD VALIGN="TOP">Clackmannanshire</TD>
|
||||||
|
<TD VALIGN="TOP">Central</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="DNB.html">Dunbartonshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">DNB</TD>
|
||||||
|
<TD VALIGN="TOP">Dunbartonshire</TD>
|
||||||
|
<TD VALIGN="TOP">Strathclyde</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="DFS.html">Dumfriesshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">DFS</TD>
|
||||||
|
<TD VALIGN="TOP">Dumfriesshire</TD>
|
||||||
|
<TD VALIGN="TOP">Dumfries and Galloway</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="ELN.html">East Lothian</A></TD>
|
||||||
|
<TD VALIGN="TOP">ELN</TD>
|
||||||
|
<TD VALIGN="TOP">East Lothian</TD>
|
||||||
|
<TD VALIGN="TOP">Lothian</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="FIF.html">Fife</A></TD>
|
||||||
|
<TD VALIGN="TOP">FIF</TD>
|
||||||
|
<TD VALIGN="TOP">Fife</TD>
|
||||||
|
<TD VALIGN="TOP">Fife</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="INV.html">Inverness-shire</A> <A HREF="#SN4">(4)</A></TD>
|
||||||
|
<TD VALIGN="TOP">INV</TD>
|
||||||
|
<TD VALIGN="TOP">Inverness-shire</TD>
|
||||||
|
<TD VALIGN="TOP">Highland<BR>
|
||||||
|
Western Isles</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="KCD.html">Kincardineshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">KCD</TD>
|
||||||
|
<TD VALIGN="TOP">Kincardineshire</TD>
|
||||||
|
<TD VALIGN="TOP">Grampian</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="KRS.html">Kinross-shire</A></TD>
|
||||||
|
<TD VALIGN="TOP">KRS</TD>
|
||||||
|
<TD VALIGN="TOP">Kinross-shire</TD>
|
||||||
|
<TD VALIGN="TOP">Tayside</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="KKD.html">Kirkcudbrightshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">KKD</TD>
|
||||||
|
<TD VALIGN="TOP">Kirkcudbrightshire</TD>
|
||||||
|
<TD VALIGN="TOP">Dumfries and Galloway</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="LKS.html">Lanarkshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">LKS</TD>
|
||||||
|
<TD VALIGN="TOP">Lanarkshire<BR>
|
||||||
|
<I>Glasgow (C)</I></TD>
|
||||||
|
<TD VALIGN="TOP">Strathclyde</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="MLN.html">Midlothian</A></TD>
|
||||||
|
<TD VALIGN="TOP">MLN</TD>
|
||||||
|
<TD VALIGN="TOP">Midlothian<BR>
|
||||||
|
<I>Edinburgh (C)</I></TD>
|
||||||
|
<TD VALIGN="TOP">Lothian<BR>
|
||||||
|
Borders</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="MOR.html">Moray</A></TD>
|
||||||
|
<TD VALIGN="TOP">MOR</TD>
|
||||||
|
<TD VALIGN="TOP">Moray</TD>
|
||||||
|
<TD VALIGN="TOP">Grampian<BR>
|
||||||
|
Highland</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="NAI.html">Nairnshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">NAI</TD>
|
||||||
|
<TD VALIGN="TOP">Nairnshire</TD>
|
||||||
|
<TD VALIGN="TOP">Highland</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="OKI.html">Orkney</A> <A HREF="#SN5">(5)</A></TD>
|
||||||
|
<TD VALIGN="TOP">OKI</TD>
|
||||||
|
<TD VALIGN="TOP">Orkney</TD>
|
||||||
|
<TD VALIGN="TOP">Orkney</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="PEE.html">Peeblesshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">PEE</TD>
|
||||||
|
<TD VALIGN="TOP">Peeblesshire</TD>
|
||||||
|
<TD VALIGN="TOP">Borders</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="PER.html">Perthshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">PER</TD>
|
||||||
|
<TD VALIGN="TOP">Perthshire</TD>
|
||||||
|
<TD VALIGN="TOP">Tayside<BR>
|
||||||
|
Central</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="RFW.html">Renfrewshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">RFW</TD>
|
||||||
|
<TD VALIGN="TOP">Renfrewshire</TD>
|
||||||
|
<TD VALIGN="TOP">Strathclyde</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="ROC.html">Ross and Cromarty</A> <A HREF="#SN6">(6)</A></TD>
|
||||||
|
<TD VALIGN="TOP">ROC</TD>
|
||||||
|
<TD VALIGN="TOP">Ross and Cromarty</TD>
|
||||||
|
<TD VALIGN="TOP">Highland<BR>
|
||||||
|
Western Isles</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="ROX.html">Roxburghshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">ROX</TD>
|
||||||
|
<TD VALIGN="TOP">Roxburghshire</TD>
|
||||||
|
<TD VALIGN="TOP">Borders</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="SEL.html">Selkirkshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">SEL</TD>
|
||||||
|
<TD VALIGN="TOP">Selkirkshire</TD>
|
||||||
|
<TD VALIGN="TOP">Borders</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="SHI.html">Shetland</A> <A HREF="#SN7">(7)</A></TD>
|
||||||
|
<TD VALIGN="TOP">SHI</TD>
|
||||||
|
<TD VALIGN="TOP">Shetland</TD>
|
||||||
|
<TD VALIGN="TOP">Shetland</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="STI.html">Stirlingshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">STI</TD>
|
||||||
|
<TD VALIGN="TOP">Stirlingshire</TD>
|
||||||
|
<TD VALIGN="TOP">Central<BR>
|
||||||
|
Strathclyde</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="SUT.html">Sutherland</A></TD>
|
||||||
|
<TD VALIGN="TOP">SUT</TD>
|
||||||
|
<TD VALIGN="TOP">Sutherland</TD>
|
||||||
|
<TD VALIGN="TOP">Highland</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="WLN.html">West Lothian</A></TD>
|
||||||
|
<TD VALIGN="TOP">WLN</TD>
|
||||||
|
<TD VALIGN="TOP">West Lothian</TD>
|
||||||
|
<TD VALIGN="TOP">Lothian<BR>
|
||||||
|
Central</TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP"><A HREF="WIG.html">Wigtownshire</A></TD>
|
||||||
|
<TD VALIGN="TOP">WIG</TD>
|
||||||
|
<TD VALIGN="TOP">Wigtownshire</TD>
|
||||||
|
<TD VALIGN="TOP">Dumfries and Galloway</TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
</CENTER></P>
|
||||||
|
|
||||||
|
<P>The links in the following table are to maps provided by the Scottish Office.</P>
|
||||||
|
|
||||||
|
<P><CENTER><TABLE BORDER="1" CELLSPACING="2" CELLPADDING="0">
|
||||||
|
<TR>
|
||||||
|
<TH COLSPAN="3">Scotland - changes of 1996</TH></TR>
|
||||||
|
<TR>
|
||||||
|
<TH>Administration 1975-1996</TH>
|
||||||
|
<TH>CCC</TH>
|
||||||
|
<TH>Successor Unitary Authorities</TH></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Borders</TD>
|
||||||
|
<TD VALIGN="TOP">BOR</TD>
|
||||||
|
<TD VALIGN="TOP"><I>The Scottish Borders (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Central</TD>
|
||||||
|
<TD VALIGN="TOP">CEN</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Clackmannanshire (U)</I><BR>
|
||||||
|
<I>Falkirk (U)</I><BR>
|
||||||
|
<I>Stirling (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Dumfries and Galloway</TD>
|
||||||
|
<TD VALIGN="TOP">DGY</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Dumfries and Galloway (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Fife</TD>
|
||||||
|
<TD VALIGN="TOP">FIF</TD>
|
||||||
|
<TD VALIGN="TOP">Fife (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Grampian</TD>
|
||||||
|
<TD VALIGN="TOP">GMP</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Aberdeenshire (U)</I><BR>
|
||||||
|
<I>Aberdeen City (U)</I><BR>
|
||||||
|
<I>Moray (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Highland <A HREF="#SN8">(8)</A></TD>
|
||||||
|
<TD VALIGN="TOP">HLD</TD>
|
||||||
|
<TD VALIGN="TOP">Highland (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Lothian</TD>
|
||||||
|
<TD VALIGN="TOP">LTN</TD>
|
||||||
|
<TD VALIGN="TOP"><I>City of Edinburgh (U)</I><BR>
|
||||||
|
<I>East Lothian (U)</I><BR>
|
||||||
|
<I>Midlothian (U)</I><BR>
|
||||||
|
<I>West Lothian (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Orkney <A HREF="#SN5">(5)</A></TD>
|
||||||
|
<TD VALIGN="TOP">OKI</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Orkney Islands (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Shetland <A HREF="#SN7">(7)</A></TD>
|
||||||
|
<TD VALIGN="TOP">SHI</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Shetland Islands (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Strathclyde <A HREF="#SN9">(9)</A></TD>
|
||||||
|
<TD VALIGN="TOP">STD</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Argyll and Bute (U)</I><BR>
|
||||||
|
<I>City of Glasgow (U)</I><BR>
|
||||||
|
<I>East Ayrshire (U)</I><BR>
|
||||||
|
<I>East Dunbartonshire (U)</I><BR>
|
||||||
|
<I>East Renfrewshire (U)</I><BR>
|
||||||
|
<I>Inverclyde (U)</I><BR>
|
||||||
|
<I>North Ayrshire (U)</I><BR>
|
||||||
|
<I>North Lanarkshire (U)</I><BR>
|
||||||
|
<I>Renfrewshire (U)</I><BR>
|
||||||
|
<I>South Ayrshire (U)</I><BR>
|
||||||
|
<I>South Lanarkshire (U)</I><BR>
|
||||||
|
<I>West Dunbartonshire (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Tayside</TD>
|
||||||
|
<TD VALIGN="TOP">TAY</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Angus (U)</I><BR>
|
||||||
|
<I>Dundee City (U)</I><BR>
|
||||||
|
<I>Perth and Kinross (U)</I></TD></TR>
|
||||||
|
<TR>
|
||||||
|
<TD VALIGN="TOP">Western Isles <A HREF="#SN10">(10)</A></TD>
|
||||||
|
<TD VALIGN="TOP">WIS</TD>
|
||||||
|
<TD VALIGN="TOP"><I>Western Isles (U)</I></TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
</CENTER></P>
|
||||||
|
|
||||||
|
<H4>Notes</H4>
|
||||||
|
|
||||||
|
<OL>
|
||||||
|
<LI><A NAME="SN1"></A>An old name for Angus is "Forfarshire".
|
||||||
|
<LI><A NAME="SN2"></A>Includes islands: Islay, Jura and Mull.
|
||||||
|
<LI><A NAME="SN3"></A>Consists of islands Arran and Bute.
|
||||||
|
<LI><A NAME="SN4"></A>Includes islands: Lewis (part), North Uist, South
|
||||||
|
Uist, and Skye.
|
||||||
|
<LI><A NAME="SN5"></A>Also "Orkney Isles", or "Orkney Islands",
|
||||||
|
but NOT "The Orkneys"!
|
||||||
|
<LI><A NAME="SN6"></A>Includes part of the island of Lewis.
|
||||||
|
<LI><A NAME="SN7"></A>Also "Shetland Isles", or "Shetland
|
||||||
|
Islands", but NOT "The Shetlands"! Originally known as
|
||||||
|
"Zetland".
|
||||||
|
<LI><A NAME="SN8"></A>Includes the island of Skye.
|
||||||
|
<LI><A NAME="SN9"></A>Includes islands: Arran, Bute, Islay, Jura and Mull.
|
||||||
|
<LI><A NAME="SN10"></A>Includes islands: Lewis, North Uist and South Uist.
|
||||||
|
</OL>
|
||||||
|
|
||||||
|
<H5><A HREF="#top">Return to top of page</A></H5>
|
||||||
|
|
||||||
|
<P>© GENUKI and Contributors 1993, 1997</P>
|
||||||
|
<HR>
|
||||||
|
<P><I>Page created by Phil Lloyd in January 1993. Revised and updated in
|
||||||
|
September 1997 by Brian Pears.</I></P>
|
||||||
|
|
||||||
|
<P><I>[Last updated: 13th February 1999 - Brian Pears]</I></P>
|
||||||
|
</BODY>
|
||||||
|
</HTML>
|
||||||
|
|
||||||
@ -0,0 +1,233 @@
|
|||||||
|
<HTML><HEAD><TITLE>Devolution referendum 97 result</TITLE></HEAD><BODY BGCOLOR=#FFFFFF>
|
||||||
|
|
||||||
|
<h1 align=center>Devolution referendum 97 result</h1><HR><CENTER><TABLE>
|
||||||
|
<tr align=center><td><IMG SRC="images/shield.gif" ALT="saltire shield"></td><td>'The reason we need a parliament in Scotland is partly so that we can repair some of the damage done by the last Government to, for example, the health service and our manufacturing industry, and partly to ensure that anti-democratic experiments like using Scotland to rehearse the poll tax can never happen again.'<BR>
|
||||||
|
The Duke of Hamilton & Brandon, whose ancestors resisted the 1707 Treaty of Union, 9 th September 1997.
|
||||||
|
</td><td><IMG SRC="images/rampant.gif" ALT="Lion Rampant"></td></tr></table></center>
|
||||||
|
<HR>
|
||||||
|
<H2 align=center>Devolution referendum 1997 - the results</H2>
|
||||||
|
(See the note below concerning the Fife count by David Boothroyd).<P>
|
||||||
|
<CENTER>
|
||||||
|
<IMG SRC="images/ballotpaper.jpg" ALT="Ballot paper"><P>
|
||||||
|
|
||||||
|
<H2>Final votes</H2>
|
||||||
|
<table border>
|
||||||
|
<tr align=center><td>I agree that there should be a Scottish Parliament</td><td>1,775,045</td><td>74.3 %</td></tr>
|
||||||
|
|
||||||
|
<tr align=center><td>I do not agree that there should be a Scottish Parliament</td><td>614,400</td><td>25.7 %</td></tr>
|
||||||
|
</table><P>
|
||||||
|
|
||||||
|
<table border>
|
||||||
|
<tr align=center><td>I agree that a Scottish Parliament should have tax-varying powers</td><td>1,512,889</td><td>63.5 %</td></tr>
|
||||||
|
|
||||||
|
<tr align=center><td>I do not agree that a Scottish Parliament should have tax-varying powers</td><td>870,263</td><td>36.5 %</td></tr>
|
||||||
|
</table>
|
||||||
|
|
||||||
|
<H2>Votes by Unitary Authority</H2>
|
||||||
|
|
||||||
|
<H3>I agree that there should be a Scottish Parliament</H3>
|
||||||
|
<TABLE BORDER >
|
||||||
|
|
||||||
|
<TR align=center><TH>Authority</TH><TH>Yes votes</TH><TH>Yes %</TH><TH>No votes</TH><TH>No %</TH></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Orkney</TH><TD>4,749</TD><TD>57.3 %</TD><TD>3,541</TD><TD>42.7 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Dumfries & Galloway</TH><TD>44,619</TD><TD>60.7 %</TD><TD>28,863</TD><TD>39.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Perthshire & Kinross</TH><TD>40,344</TD><TD>61.7 %</TD><TD>24,998</TD><TD>38.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Renfrewshire</TH><TD>28,253</TD><TD>61.7 %</TD><TD>17,573</TD><TD>38.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Shetland</TH><TD>5,430</TD><TD>62.4 %</TD><TD>3,275</TD><TD>37.6 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Scottish Borders</TH><TD>33,855</TD><TD>62.8 %</TD><TD>20,060</TD><TD>37.2 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Aberdeenshire</TH><TD>61,621</TD><TD>63.9 %</TD><TD>34,878</TD><TD>36.1 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Angus</TH><TD>33,571</TD><TD>64.7 %</TD><TD>18,350</TD><TD>35.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>South Ayrshire</TH><TD>40,161</TD><TD>66.9 %</TD><TD>19,909</TD><TD>33.1 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Moray</TH><TD>24,822</TD><TD>67.2 %</TD><TD>12,122</TD><TD>32.8 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Argyll & Bute</TH><TD>30,452</TD><TD>67.3 %</TD><TD>14,796</TD><TD>32.7 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Stirling</TH><TD>29,190</TD><TD>68.5 %</TD><TD>13,440</TD><TD>31.5 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Dunbartonshire</TH><TD>40,917</TD><TD>69.8 %</TD><TD>17,725</TD><TD>30.2 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Aberdeen</TH><TD>65,035</TD><TD>71.8 %</TD><TD>25,580</TD><TD>28.2 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Edinburgh</TH><TD>155,900</TD><TD>71.9 %</TD><TD>60,832</TD><TD>28.1 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Highland</TH><TD>72,551</TD><TD>72.6 %</TD><TD>27,431</TD><TD>27.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Lothian</TH><TD>33,525</TD><TD>74.2 %</TD><TD>11,665</TD><TD>25.8 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Dundee</TH><TD>49,252</TD><TD>76.0 %</TD><TD>15,553</TD><TD>24.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Fife</TH><TD>125,668</TD><TD>76.1 %</TD><TD>39,517</TD><TD>23.9 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>North Ayrshire</TH><TD>51,304</TD><TD>76.3 %</TD><TD>15,931</TD><TD>23.7 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>South Lanarkshire</TH><TD>114,908</TD><TD>77.8 %</TD><TD>32,762</TD><TD>22.2 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Inverclyde</TH><TD>31,680</TD><TD>78.0 %</TD><TD>8,945</TD><TD>22.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Renfrewshire</TH><TD>68,711</TD><TD>79.0 %</TD><TD>18,213</TD><TD>21.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Western Isles</TH><TD>9,977</TD><TD>79.4 %</TD><TD>2,589</TD><TD>20.6 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>West Lothian</TH><TD>56,923</TD><TD>79.6 %</TD><TD>14,614</TD><TD>20.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Midlothian</TH><TD>31,681</TD><TD>79.9 %</TD><TD>7,979</TD><TD>20.1 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Clackmannanshire</TH><TD>18,790</TD><TD>80.0 %</TD><TD>4,706</TD><TD>20.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Falkirk</TH><TD>55,642</TD><TD>80.0 %</TD><TD>13,953</TD><TD>20.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Ayrshire</TH><TD>49,131</TD><TD>81.1 %</TD><TD>11,426</TD><TD>18.9 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>North Lanarkshire</TH><TD>123,063</TD><TD>82.6 %</TD><TD>26,010</TD><TD>17.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Glasgow</TH><TD>204,269</TD><TD>83.6 %</TD><TD>40,106</TD><TD>16.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>West Dunbartonshire</TH><TD>39,051</TD><TD>84.7 %</TD><TD>7,058</TD><TD>15.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Scotland</TH><TH>1,775,045</TH><TH>74.3 %</TH><TH>614,400</TH><TH>25.7 %</TH></TR>
|
||||||
|
</table><P>
|
||||||
|
|
||||||
|
<H3>I agree that a Scottish Parliament should have tax-varying powers</H3>
|
||||||
|
<TABLE BORDER >
|
||||||
|
|
||||||
|
<TR align=center><TH>Authority</TH><TH>Yes votes</TH><TH>Yes %</TH><TH>No votes</TH><TH>No %</TH></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Orkney</TH><TD>3,917</TD><TD>47.4 %</TD><TD>4,344</TD><TD>52.6 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Dumfries & Galloway</TH><TD>35,737</TD><TD>48.8 %</TD><TD>37,499</TD><TD>51.2 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Scottish Borders</TH><TD>27,284</TD><TD>50.7 %</TD><TD>26,497</TD><TD>49.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Perthshire & Kinross</TH><TD>33,398</TD><TD>51.3 %</TD><TD>31,709</TD><TD>48.7 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Renfrewshire</TH><TD>23,580</TD><TD>51.6 %</TD><TD>22,153</TD><TD>48.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Shetland</TH><TD>4,478</TD><TD>51.6 %</TD><TD>4,198</TD><TD>48.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Aberdeenshire</TH><TD>50,295</TD><TD>52.3 %</TD><TD>45,929</TD><TD>47.7 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Moray</TH><TD>19,326</TD><TD>52.7 %</TD><TD>17,344</TD><TD>47.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Angus</TH><TD>27,641</TD><TD>53.4 %</TD><TD>24,089</TD><TD>46.6 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>South Ayrshire</TH><TD>33,679</TD><TD>56.2 %</TD><TD>26,217</TD><TD>43.8 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Argyll & Bute</TH><TD>25,746</TD><TD>57.0 %</TD><TD>19,429</TD><TD>43.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Stirling</TH><TD>25,044</TD><TD>58.9 %</TD><TD>17,487</TD><TD>41.1 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Dunbartonshire</TH><TD>34,576</TD><TD>59.1 %</TD><TD>23,914</TD><TD>40.9 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Aberdeen</TH><TD>54,320</TD><TD>60.3 %</TD><TD>35,709</TD><TD>39.7 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Edinburgh</TH><TD>133,843</TD><TD>62.0 %</TD><TD>82,188</TD><TD>38.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Highland</TH><TD>61,359</TD><TD>62.1 %</TD><TD>37,525</TD><TD>37.9 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Lothian</TH><TD>28,152</TD><TD>62.7 %</TD><TD>16,765</TD><TD>37.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Renfrewshire</TH><TD>55,075</TD><TD>63.6 %</TD><TD>31,537</TD><TD>36.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Fife</TH><TD>108,021</TD><TD>64.7 %</TD><TD>58,987</TD><TD>35.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Dundee</TH><TD>42,304</TD><TD>65.5 %</TD><TD>22,280</TD><TD>34.5 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>North Ayrshire</TH><TD>43,990</TD><TD>65.7 %</TD><TD>22,991</TD><TD>34.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Inverclyde</TH><TD>27,194</TD><TD>67.2 %</TD><TD>13,277</TD><TD>32.8 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>West Lothian</TH><TD>47,990</TD><TD>67.3 %</TD><TD>23,354</TD><TD>32.7 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>South Lanarkshire</TH><TD>99,587</TD><TD>67.6 %</TD><TD>47,708</TD><TD>32.4 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Midlothian</TH><TD>26,776</TD><TD>67.7 %</TD><TD>12,762</TD><TD>32.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Western Isles</TH><TD>8,557</TD><TD>68.4 %</TD><TD>3,947</TD><TD>31.6 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Clackmannanshire</TH><TD>16,112</TD><TD>68.7 %</TD><TD>7,355</TD><TD>31.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Falkirk</TH><TD>48,064</TD><TD>69.2 %</TD><TD>21,403</TD><TD>30.8</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>East Ayrshire</TH><TD>42,559</TD><TD>70.5 %</TD><TD>17,824</TD><TD>29.5 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>North Lanarkshire</TH><TD>107,288</TD><TD>72.2 %</TD><TD>41,372</TD><TD>27.8 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>West Dunbartonshire</TH><TD>34,408</TD><TD>74.7 %</TD><TD>11,628</TD><TD>25.3 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Glasgow</TH><TD>182,589</TD><TD>75.0 %</TD><TD>60,842</TD><TD>25.0 %</TD></TR>
|
||||||
|
|
||||||
|
<TR align=center><TH>Scotland</TH><TH>1,512,889</TH><TH>63.5 %</TH><TH>870,263</TH><TH>36.5 %</TH></TR>
|
||||||
|
</table>
|
||||||
|
|
||||||
|
<HR>
|
||||||
|
<p>
|
||||||
|
<H2 align=center>How Scotland voted, region by region, in 1979</H2>
|
||||||
|
|
||||||
|
<table border>
|
||||||
|
<tr align=center><th>Region/Islands area</th><th>Yes Votes</th><th>% votes</th><th>% electorate</th><th>No Votes</th><th>% votes</th><th>% electorate</th><th>Turnout</th></tr>
|
||||||
|
<tr align=center><td>Shetland Islands</td> <td>2,020</td><td>27</td><td>14</td><td>5,466</td><td>73</td><td>36</td><td>50</td></tr>
|
||||||
|
<tr align=center><td>Orkney Islands</td><td>2,104</td><td>28</td><td>15</td> <td>5,439</td><td>72</td><td>39</td><td>54</td></tr>
|
||||||
|
<tr align=center><td>Borders</td> <td>20,746</td><td>40</td><td>27</td> <td>30,780</td><td>60</td><td>40</td><td>67</td></tr>
|
||||||
|
<tr align=center><td>Dumfries & Galloway</td> <td>27,162</td><td>40</td><td>26</td> <td>40,239</td><td>60</td><td>38</td><td>64</td></tr>
|
||||||
|
<tr align=center><td>Grampian</td> <td>94,944</td><td>48</td><td>28</td><td>101,485</td><td>52</td><td>30</td><td>58</td></tr>
|
||||||
|
<tr align=center><td>Tayside</td> <td>91,482</td><td>49</td><td>31</td><td>93,325</td><td>51</td><td>32</td><td>63</td></tr>
|
||||||
|
<tr align=center><td>Lothian</td> <td>187,221</td><td>50</td><td>33</td><td>186,421</td><td>50</td><td>33</td><td>66</td></tr>
|
||||||
|
<tr align=center><td>Highland <td>44,973</td><td>51</td><td>33</td> <td>43,274</td><td>49</td><td>32</td><td>65</td></tr>
|
||||||
|
<tr align=center><td>Fife</td><td>86,252</td><td>54</td><td>35</td> <td>74,436</td><td>46</td><td>30</td><td>65</td></tr>
|
||||||
|
<tr align=center><td>Strathclyde</td> <td>596,519</td><td>54</td><td>34</td><td>508,599</td><td>46</td><td>29</td><td>63</td></tr>
|
||||||
|
<tr align=center><td>Central</td> <td>71,296</td><td>55</td><td>36</td> <td>59,105</td><td>45</td><td>30</td><td>66</td></tr>
|
||||||
|
<tr align=center><td>Western Isles</td><td>6,218</td><td>56</td><td>28</td> <td>4,933</td><td>44</td><td>22</td><td>50</td></tr>
|
||||||
|
<tr align=center><th>Scotland</th><th>1,230,937</th><th>52</th><th>33*</th><th>1,153,502</th><th>48</th><th>31*</th><th>64*</th></tr>
|
||||||
|
</table></center>
|
||||||
|
*Percentage on register of 3,747,112 as adjusted by Secretary of State.<P>
|
||||||
|
<HR>
|
||||||
|
<H2 ALIGN=CENTER>Note by David Boothroyd concerning the Fife count</H2>
|
||||||
|
|
||||||
|
I have been doing some work developing my website (which is now at
|
||||||
|
<A HREF="http://www.election.demon.co.uk/election.html" target="popup">http://www.election.demon.co.uk/election.html</A>) and while preparing the
|
||||||
|
results of the Scottish Parliament referendum I discovered a fairly big
|
||||||
|
discrepancy in the count from Fife Council.<P>
|
||||||
|
|
||||||
|
The Scottish Office press release giving the results of the referendum
|
||||||
|
(no. 1269/97) says that 166,554 people voted in Fife, which I presume
|
||||||
|
represents the number marked on registers as voting. On the first question,
|
||||||
|
the total number of votes (Yes, No and spoilt ballot papers) is 166,025.<P>
|
||||||
|
|
||||||
|
However on the second question, the total number of votes is 167,999 -
|
||||||
|
1,445 more than the number of ballot papers which should have been issued,
|
||||||
|
and 1,974 more than the number of ballot papers counted on the first question.<P>
|
||||||
|
|
||||||
|
All sources of results give the same figures and so I wrote to the Scottish
|
||||||
|
Office to ask them how this discrepancy might have come about. Their reply
|
||||||
|
suggests it may have resulted from voters demanding only the ballot paper
|
||||||
|
for the second question, though presiding officers were instructed to give
|
||||||
|
all voters both ballot papers, and such people would be marked as voting
|
||||||
|
and therefore included anyway.<P>
|
||||||
|
|
||||||
|
The Scottish Office verified that the results which were issued were those
|
||||||
|
which were certified by the counting officer in Fife and so they represent
|
||||||
|
the result of the referendum in spite of being inaccurate.<P>
|
||||||
|
|
||||||
|
<HR>
|
||||||
|
If anyone can shed any light on this please contact David Boothroyd at <A HREF="mailto:david@election.demon.co.uk">david@election.demon.co.uk</A><P>
|
||||||
|
|
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|
<HR>
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||||||
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|
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@ -0,0 +1,234 @@
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<html>
|
||||||
|
<head><title>Scottish Economic Bulletin: Economic Review</title></head>
|
||||||
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<body background="/img/backgrn1.gif">
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<div align=center>
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<table width=70%><tr><td>
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<table cellpadding=0 cellspacing=0 width=100% border=0>
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<tr><td>
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||||||
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<table cellpadding=0 cellspacing=0 width=100%><tr><td><font size=-1><a href="seb.htm">Scottish Economic Bulletin</a></font></td><td align=right><font size=-1> </font></td></tr></table></td></tr>
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||||||
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<tr><td><hr></td></tr>
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||||||
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<tr><td>
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<table width=100% cellpadding=2 cellspacing=0>
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<tr><td><br> <br></td></tr>
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||||||
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<tr><td valign=top>
|
||||||
|
<h3>The Scottish Economy</h3>
|
||||||
|
<b>Gross Domestic Product</b>
|
||||||
|
<P>
|
||||||
|
Provisional estimates of GDP (income measure) for each UK Government Office Region/country are now available for 1996 with the publication of the Regional Accounts.<a name="note-7"></A><font size=-2><sup><a href="#notes">7</a></sup></font> Estimates for 1995 were also made available at county/former Scottish region level.
|
||||||
|
<P>
|
||||||
|
Scottish GDP in 1996 was £54.43 billion, 8.6 per cent of UK GDP. GDP per head was £10,614, 99.1 per cent of the UK average. This was the fourth highest of the 12 UK Government Office Regions/countries - below only London, South East and Eastern - for the fifth successive year.
|
||||||
|
<P>
|
||||||
|
GDP per head in Scotland relative to the UK increased strongly between 1989 and 1992, reflecting the stronger performance of the Scottish economy in the 1990-1992 UK recession. Since 1992, GDP per head has fluctuated around 99 per cent of UK GDP per head, reaching a peak of 100.2 per cent in 1995.
|
||||||
|
<P>
|
||||||
|
Table 2 shows GDP per head in the former Scottish regions in 1995.<a name="note-8"></A><font size=-2><sup><a href="#notes">8</a></sup></font> It is instructive to look at trends and, accordingly, Table 2 also provides data for 1989. GDP per head was well above the UK average in both Grampian (133 per cent) and Lothian (124 per cent) in 1995. Although Grampian showed the smallest increase in GDP per head over the 1993-1995 period (and fell slightly relative to the UK), the level of GDP per head was third only to London and Berkshire across the UK, followed by Lothian. All other Scottish regions were below the UK average and GDP per head in the Highlands and Islands and in Fife was amongst the lowest in the UK.
|
||||||
|
<P>
|
||||||
|
<b>Table 2: GDP in the Scottish Regions, 1989 and 1995</b>
|
||||||
|
<P>
|
||||||
|
<TABLE border>
|
||||||
|
<TR valign=top><TD rowspan=2> </TD><TD rowspan=2><b>GDP per head 1995 (£)</b></TD><TD colspan=2><b>GDP per head, 1990=100</b></TD></TR>
|
||||||
|
<TR><TD><b>1989</b></TD><TD><b>1995</b></TD></TR>
|
||||||
|
<TR><TD>Borders</TD><TD>9,003</TD><TD>80.1</TD><TD>88.3</TD></TR>
|
||||||
|
<TR><TD>Central</TD><TD>9,265</TD><TD>89.1</TD><TD>90.8</TD></TR>
|
||||||
|
<TR><TD>Dumfries and Galloway</TD><TD>9,555</TD><TD>86.4</TD><TD>93.7</TD></TR>
|
||||||
|
<TR><TD>Fife</TD><TD>8,314</TD><TD>84.0</TD><TD>81.5</TD></TR>
|
||||||
|
<TR><TD>Grampian</TD><TD>13,566</TD><TD>119.4</TD><TD>133.0</TD></TR>
|
||||||
|
<TR><TD>Highlands and Islands</TD><TD>8,298</TD><TD>80.4</TD><TD>81.4</TD></TR>
|
||||||
|
<TR><TD>Lothian</TD><TD>12,656</TD><TD>111.4</TD><TD>124.1</TD></TR>
|
||||||
|
<TR><TD>Strathclyde</TD><TD>9,483</TD><TD>87.9</TD><TD>93.0</TD></TR>
|
||||||
|
<TR><TD>Tayside</TD><TD>9,611</TD><TD>88.8</TD><TD>94.2</TD></TR>
|
||||||
|
<TR><TD></TD><TD></TD><TD></TD></TR>
|
||||||
|
<TR><TD><b>Scotland</b></TD><TD><b>10,244</b></TD><TD><b>93.8</b></TD><TD><b>100.2</b></TD></TR>
|
||||||
|
<TR><TD></TD><TD></TD><TD></TD></TR>
|
||||||
|
<TR><TD>UK</TD><TD>10,199</TD><TD>100.0</TD><TD>100.0</TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
<BR>
|
||||||
|
Source: Office for National Statistics
|
||||||
|
<P>
|
||||||
|
The improvement in Scottish GDP per head, relative to the UK, from 1989 has been evident across most Scottish regions. Lothian, Borders and Grampian have seen particularly marked improvements and only Fife had a lower relative level of GDP per head in 1995 than in 1989. Relative GDP per head in the Highlands and Islands has increased slightly but levels have fallen since the peak (of 88.8 per cent ) in 1991.
|
||||||
|
<P>
|
||||||
|
<h3>Index of Production and Construction</h3>
|
||||||
|
The Scottish Office Education and Industry Department's quarterly Index of Production and Construction rose by 0.4 per cent in 1997 Q3. Excluding oil and gas, the Index rose by 0.5 per cent. At a broad sectoral level, output rose in manufacturing (0.9 per cent) and in electricity, gas and water supply (5.7 per cent), offset by falling output in construction (2.5 per cent) and mining and quarrying (1.8 per cent). The UK index (less oil and gas) rose by 0.7 per cent in 1997 Q3.
|
||||||
|
<P>
|
||||||
|
An indication of the underlying trend in industrial output is obtained by comparing the last 4 quarters for which data are available (to 1997 Q3) with the previous 4 quarters (to 1996 Q3). Excluding oil and gas, the Index rose by 6.0 per cent over this period, as increases were recorded in manufacturing (7.4 per cent), construction (1.8 per cent), electricity, gas and water supply (5.7 per cent) and mining and quarrying (3.3 per cent). By comparison, the UK Index (less oil and gas) rose by 2.0 per cent over the same period.
|
||||||
|
<P>
|
||||||
|
Since 1990, manufacturing output has increased by 25.6 per cent. Growth in UK manufacturing has been much more sluggish than in Scotland, growing by only 5.1 per cent over the same period. The influence of the electrical and instrument engineering sector (EIE) on Scottish manufacturing has been discussed in past editions of the Scottish Economic Bulletin and by outside commentators. Excluding EIE, manufacturing output in Scotland has declined by 7.7 per cent since 1990. UK manufacturing excluding EIE has increased by 1.8 per cent.
|
||||||
|
<P>
|
||||||
|
In the year to 1997 Q3, the EIE sector continued to grow strongly - by 18.4 per cent. However, growth was also evident in 6 of the other 10 manufacturing sectors over the period. This is the continuation of a trend over the last year in which growth in the manufacturing sector has become more broadly based. Indeed as Chart 3 shows, manufacturing output excluding EIE has been increasing year-on-year in each quarter since 1996 Q4, a trend not seen since 1990 Q3. In the year to 1997 Q3, manufacturing output excluding EIE grew by 1.4 per cent, only slightly below the 1.5 per cent growth in the UK as a whole.
|
||||||
|
<P>
|
||||||
|
<A NAME="chart3"></A>
|
||||||
|
<A HREF="chart3.htm">CHART 3 HERE</A>
|
||||||
|
<P>
|
||||||
|
<b>Exports</b>
|
||||||
|
<P>
|
||||||
|
The manufacturing sector accounts for most of Scotland's external trade with the rest of the world. Estimates from the 1994 Input-Output Tables<a name="note-9"></A><font size=-2><sup><a href="#notes">9</a></sup></font>9 indicate that around three quarters of trade is in manufacturing. The Scottish Council Development and Industry (SCDI) annual survey of Scottish Manufactured Exports for 1996 was published in December 1997. In current prices, the value of Scottish manufactured exports<a name="note-10"></A><font size=-2><sup><a href="#notes">10</a></sup></font> was estimated to have risen by 6.4 per cent in 1996 to £18.42 billion. This represents a slower rate of growth than in recent years (20.3 per cent in 1995 and 24.8 per cent in 1994) and can be compared with growth of 8.9 per cent in UK manufactured exports (to £155.18 billion) in 1996. For the first time since 1988, UK manufactured exports growth outpaced that of Scotland and Scotland's share of UK exports fell marginally from 12.1 per cent in 1995 to 11.9 per cent in 1996.
|
||||||
|
<P>
|
||||||
|
As shown in Table 3, four sectors - Office Machinery, Radio/TV/Communication Equipment, Whisky and Chemicals - continued to dominate Scottish manufactured exports in 1996, accounting for 75 per cent of the total. The electronics sector<a name="note-11"></A><font size=-2><sup><a href="#notes">11</a></sup></font> had a more mixed export performance in 1996 than in recent years. Exports grew by 6.9 per cent to £10.21 billion (55.5 per cent of total manufactured exports). This compares with growth of over 42 per cent in 1995. Exports from the Office Machinery sector - the largest exporting sector - rose by 14.3 per cent in 1996 to £6.83 billion (37.1 per cent of total manufactured exports). While this rate of growth was considerably lower than in 1995, the sector still contributed over 77 per cent to the total growth in manufactured exports in 1996. Exports from the other major element of Scotland's electronics industry - the Radio/TV/Communication Equipment sector - declined by 7.3 per cent to £3.00 billion.
|
||||||
|
<P>
|
||||||
|
<b>Table 3: Top Exporting Sectors in Scotland, 1996</b>
|
||||||
|
<P>
|
||||||
|
<TABLE border>
|
||||||
|
<TR valign=top><TD><b>Sector (SIC92)</b></TD><td><b>Value at current prices (£ million)</b></td><td><b>Per cent of Total</b></td><td><b>Nominal increase in value 1995-96: per cent</b></td><td><b>Contribution to total export growth: per cent</b></td></TR>
|
||||||
|
<TR><TD>Office Machinery</TD><TD>6,825.0</TD><TD>37.1</TD><TD>14.3</TD><TD>77.5</TD></TR>
|
||||||
|
<TR><TD>Radio, Television & Communication</TD><TD>3,003.8</TD><TD>16.3</TD><TD>-7.3</TD><TD>-21.6</TD></TR>
|
||||||
|
<TR><TD> Equipment and Apparatus Whisky</TD><TD>2,278.1</TD><TD>12.4</TD><TD>0.1</TD><TD>0.1</TD></TR>
|
||||||
|
<TR><TD>Chemicals and Chemical Products</TD><TD>1,706.4</TD><TD>9.3</TD><TD>9.2</TD><TD>13.1</TD></TR>
|
||||||
|
<TR><TD>Machinery and Equipment nec</TD><TD>802.2</TD><TD>4.4</TD><TD>18.4</TD><TD>11.4</TD></TR>
|
||||||
|
<TR><TD>Other Food Products & Beverages</TD><TD>446.0</TD><TD>2.4</TD><TD>-10.7</TD><TD>-4.8</TD></TR>
|
||||||
|
<TR><TD>Fabricated Metal Products except Machinery and Equipment</TD><TD>411.1</TD><TD>2.2</TD><TD>37.3</TD><TD>10.2</TD></TR>
|
||||||
|
<TR><TD>Pulp, Paper and Paper Products</TD><TD>387.0</TD><TD>2.1</TD><TD>-2.0</TD><TD>-0.7</TD></TR>
|
||||||
|
<TR><TD>Coke, Refined Petroleum Products and Nuclear Fuel</TD><TD>332.0</TD><TD>1.8</TD><TD>69.6</TD><TD>12.4</TD></TR>
|
||||||
|
<TR><TD>Other Transport Equipment</TD><TD>326.8</TD><TD>1.8</TD><TD>-22.4</TD><TD>-8.6</TD></TR>
|
||||||
|
<TR><TD></TD><TD></TD><TD></TD><TD></TD></TR>
|
||||||
|
<TR><TD>Other sectors</TD><TD>1,896.2</TD><TD>10.3</TD><TD>6.8</TD><TD>11.1</TD></TR>
|
||||||
|
<TR><TD></TD><TD></TD><TD></TD><TD></TD></TR>
|
||||||
|
<TR><TD><b>All Manufacturing Industries</b></TD><TD><b>18,414.6</b></TD><TD><b>100.0</b></TD><TD><b>6.3</b></TD><TD><b>100.0</b></TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
<BR>
|
||||||
|
Source: Scottish Council Development and Industry
|
||||||
|
<P>
|
||||||
|
Note: 1. Under SIC 92 Whisky is normally incorporated in the Food Products & Beverages sector.
|
||||||
|
<P>
|
||||||
|
Exports from the whisky sector increased only marginally in 1996, up by 0.1 per cent to £2.28 billion (12.4 per cent of total manufactured exports). The Chemicals and Chemical Products sector experienced a further rise in exports in 1996, of 9.2 per cent to £1.71 billion (9.3 per cent of total manufactured exports). This follows growth of 9.0 per cent in 1995. An additional 19 industry sectors together represented 25 per cent of total manufactured exports in 1996. Export growth was recorded in 14 sectors.
|
||||||
|
<P>
|
||||||
|
Overall the latest figures record a positive - and better than expected - performance by Scottish manufacturing in export markets during 1996. The SCDI quarterly index based on a selected panel survey of large exporters had provisionally estimated a fall of 6.8 per cent in manufactured exports. The rapid growth rates of recent years have slowed but export levels in most sectors continue to rise. Initial estimates from the SCDI quarterly index for 1997 suggest further growth of 12.0 per cent to £20.61 billion.
|
||||||
|
<P>
|
||||||
|
<b>Exports by Destination</b>
|
||||||
|
<P>
|
||||||
|
As shown in Table 4, the EU remained Scotland's main trading area in 1996 with a 58 per cent share of Scotland's exports. However, exports grew more modestly - by 2.9 per cent - in 1996. Six of the top ten individual country markets were in the EU, the others being the USA, Japan, Switzerland and Norway. The latest survey results confirm France as Scotland's largest export market for the fourth successive year, despite a drop in the actual value of exports of 5.4 per cent to £2.80 billion. (15.2 per cent of total Scottish manufactured exports).
|
||||||
|
<P>
|
||||||
|
<b>Table 4: Destination of Scottish Exports in 1996</b>
|
||||||
|
<P>
|
||||||
|
<TABLE border>
|
||||||
|
<TR valign=top><TD> </TD><TD><b>Value (£ million, current prices)</b></TD><TD><b>Per cent of total</b></TD><TD><b>Nominal percentage growth in 1996</b></TD><TD><b>Contribution to overall growth: per cent</b></TD></TR>
|
||||||
|
<TR><TD>European Union</TD><TD>10,756</TD><TD>58.4</TD><TD>2.9</TD><TD>27.5</TD></TR>
|
||||||
|
<TR><TD>North America</TD><TD>2,318</TD><TD>12.6</TD><TD>36.8</TD><TD>56.8</TD></TR>
|
||||||
|
<TR><TD>Other Asia Pacific</TD><TD>1,556</TD><TD>8.4</TD><TD>-14.2</TD><TD>-23.5</TD></TR>
|
||||||
|
<TR><TD>EFTA</TD><TD>1,072</TD><TD>5.8</TD><TD>16.4</TD><TD>13.7</TD></TR>
|
||||||
|
<TR><TD>Japan</TD><TD>812</TD><TD>4.4</TD><TD>6.3</TD><TD>4.4</TD></TR>
|
||||||
|
<TR><TD>Middle East</TD><TD>513</TD><TD>2.8</TD><TD>23.3</TD><TD>8.8</TD></TR>
|
||||||
|
<TR><TD>Latin America</TD><TD>510</TD><TD>2.8</TD><TD>4.5</TD><TD>2.0</TD></TR>
|
||||||
|
<TR><TD>Eastern Europe</TD><TD>396</TD><TD>2.2</TD><TD>53.5</TD><TD>12.6</TD></TR>
|
||||||
|
<TR><TD>Africa</TD><TD>311</TD><TD>1.7</TD><TD>-0.6</TD><TD>-0.2</TD></TR>
|
||||||
|
<TR><TD>Australasia</TD><TD>171</TD><TD>0.9</TD><TD>-11.9</TD><TD>-2.1</TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
<BR>
|
||||||
|
Source: Scottish Council Development and Industry
|
||||||
|
<P>
|
||||||
|
Exports to the USA rose by 38.3 per cent in 1996 to £2.22 billion. The USA was responsible for nearly 50 per cent of the increase in total Scottish exports and overtook Germany as the second largest market. There was a strong upturn in sales across the Office Machinery, Radio/TV/Communication Equipment, Coke/Petroleum and Chemicals sectors; the strength of the US economy a causal factor. North America displaced Other Asia Pacific as Scotland's second largest trading area.
|
||||||
|
<P>
|
||||||
|
Exports to Japan continued to increase and remained the 7th largest country market for Scottish goods. Total exports to the Other Asia Pacific countries fell by 14.2 per cent in 1996, compared with strong growth of 30.9 per cent in 1995. However, this was almost entirely due to a large drop in exports to Malaysia; there were significant rises in exports to Hong Kong, Singapore and Taiwan. Elsewhere, exports to most other regions showed significant growth with sales to Eastern Europe up 53.5 per cent and exports to the Middle East up 23.3 per cent. Growth in sales were also recorded to the EFTA countries, while exports to Latin America continued to grow modestly. There was a marginal decline in sales to Africa following last year's significant increase, while exports to Australasia continued to decline.
|
||||||
|
<P>
|
||||||
|
<b>The Sterling Exchange Rate and Exports</b>
|
||||||
|
<P>
|
||||||
|
Inevitably, the strength of sterling has put pressure on Scottish exports. As one would expect, the exposure to exchange rate movements varies by sector in Scotland. This is illustrated in Table 5 which shows, at the broad sectoral level, the proportion of total domestic (i.e. Scottish) output dependent on exports outwith the UK (i.e. to the rest of the world, ROW) and the import content of that output from the same source. The table also shows the corresponding proportions for Scotland's trade with the rest of the UK (RUK).
|
||||||
|
<P>
|
||||||
|
<b>Table 5: The External Orientation of Scottish Industry, 1994</b>
|
||||||
|
<P>
|
||||||
|
<TABLE border>
|
||||||
|
<TR valign=top><TD rowspan=2 valign=top>Industry</TD><TD colspan=2><b>Proportion of domestic output dependent on :</b></TD><TD colspan=2><b>Components of gross domestic output </b></TD></TR>
|
||||||
|
<TR><TD><b>Exports to RUK</b></TD><TD><b>Exports to ROW</b></TD><TD><b>Imports from RUK</b></TD><TD><b>Imports from ROW</b></TD></TR>
|
||||||
|
<TR><TD>Agriculture, Forestry and Fishing</TD><TD>19.7</TD><TD>12.9</TD><TD>7.4</TD><TD>1.4</TD></TR>
|
||||||
|
<TR><TD>Mining and Quarrying</TD><TD>41.0</TD><TD>29.1</TD><TD>18.9</TD><TD>6.8</TD></TR>
|
||||||
|
<TR><TD>Energy and Water Supply</TD><TD>6.4</TD><TD>1.0</TD><TD>7.8</TD><TD>7.7</TD></TR>
|
||||||
|
<TR><TD>Manufacturing</TD><TD>26.7</TD><TD>41.8</TD><TD>18.8</TD><TD>18.2</TD></TR>
|
||||||
|
<TR><TD>Construction</TD><TD>6.0</TD><TD>0.0</TD><TD>17.7</TD><TD>3.9</TD></TR>
|
||||||
|
<TR><TD>Transport and Communication</TD><TD>20.0</TD><TD>8.4</TD><TD>8.9</TD><TD>2.5</TD></TR>
|
||||||
|
<TR><TD>Distribution and Catering</TD><TD>14.1</TD><TD>0.0</TD><TD>5.7</TD><TD>1.0</TD></TR>
|
||||||
|
<TR><TD>Financial and Business Services</TD><TD>12.3</TD><TD>5.8</TD><TD>10.8</TD><TD>1.9</TD></TR>
|
||||||
|
<TR><TD>Other Services</TD><TD>4.1</TD><TD>2.7</TD><TD>4.1</TD><TD>1.2</TD></TR>
|
||||||
|
<TR><TD></TD></TR>
|
||||||
|
<TR><TD><b>Whole Economy</b></TD><TD><b>16.5</b></TD><TD><b>16.3</b></TD><TD><b>12.0</b></TD><TD><b>7.3</b></TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
<BR>
|
||||||
|
Source: The Scottish Office
|
||||||
|
<P>
|
||||||
|
The manufacturing sector is clearly the most sensitive to the effects of exchange rate changes: over 40 per cent of output is exported to ROW and almost 20 per cent of inputs are imported from ROW. Within the sector (though not shown in the table), 2 industries - drink and electrical and instrument engineering - export more than two thirds of their output to ROW, while chemicals and electrical and instrument engineering also import more than a third of inputs. By contrast, the output of the service sector is much more dependent on the home market, relying less on exports to generate value added. The gross output of the service sector also embodies a lower import content.
|
||||||
|
<P>
|
||||||
|
For manufacturing, available evidence from the SCDI for 1997 suggests that the strength of sterling is causing difficulties in terms of reduced margins and some job losses. However, as described above, it appears that it has not yet impacted upon the level of export sales, only profitability.
|
||||||
|
<P>
|
||||||
|
Business survey evidence in Scotland does point to an adverse impact on exports resulting from sterling's strength but results are far from conclusive. The Scottish Chambers' Business Survey reported a decline in export orders and sales in 1997 Q4, as in Q3 and results from Scottish Engineering also revealed that export orders declined for the third successive quarter, falling in all sectors of the industry. By contrast, the CBI Industrial Trends Survey reported a return to growth in export orders and deliveries also increased significantly in the fourth quarter. However, optimism regarding export prospects fell markedly and, as one might expect, respondents continued to believe that prices would be the most important constraint on export orders over the coming months.
|
||||||
|
<P>
|
||||||
|
One particular area in which the exchange rate may have been expected to affect activity levels is travel and tourism both to and from overseas. International Passenger Survey (IPS) evidence for the 12 months to November 1997 shows that the number of visitors to the UK rose by 3 per cent, compared with the year to November 1996. The number of visits from North America increased by 14 per cent, while the number of visits from Western Europe was broadly static. Visits from Other Areas rose by 4 per cent. The total number of UK residents' visits abroad during the 12 months ending November 1997 rose by 11 per cent compared with a year earlier. Visits to Western Europe increased by 12 per cent, while visits to North America and Other Areas increased by 2 per cent and 10 per cent respectively. Overseas earnings rose by 2 per cent in current prices in the year to November and expenditure by UK residents rose by 6 per cent. This resulted in an increase in the deficit on the travel account of the balance of payments from £3.8 billion to £4.6 billion over the period.
|
||||||
|
<P>
|
||||||
|
The change in the composition of the tourism market appears to be consistent with the larger rise in sterling against the main European currencies over the last 18 months and has implications for Scotland. North America, Germany and France all account for higher proportions of overseas visits to Scotland than to the UK as a whole. However, a complicating factor is that US and French visitors tend to have a high propensity for travelling as part of a package holiday, paid for in advance with prices based on an exchange rate determined possibly months before the holiday is taken. Consequently, the impact of changes in exchange rates on visits from US and French residents may be delayed. By contrast, the principal types of Dutch and German holidaymakers to Scotland tend to travel independently and to holiday on an ad hoc basis at relatively short notice. The impact of the strength of sterling on these groups is likely to have been demonstrated relatively quickly.
|
||||||
|
<P>
|
||||||
|
Some IPS data for Scotland are available to the third quarter of 1997. The total number of overnight visits from overseas tourists was broadly unchanged in the first 3 quarters of the year, compared with the same period in 1996. However, the total from Western Europe fell by 6 per cent and overnight visits from North America were broadly unchanged. By contrast, visits from Other Areas rose by 12 per cent. Evidence for Scotland from the United Kingdom Tourism Survey, covering the first 3 quarters of 1997, reported a 3 per cent fall in the number of tourist trips to Scotland by UK residents compared with the same period in 1996. This compares with growth rates of around 15 per cent in each of the previous 2 years. The value of these trips increased by 7 per cent in current prices, broadly equal to growth in the UK over the same period but lower than growth in 1995 and 1996.
|
||||||
|
<P>
|
||||||
|
<b>Labour Market</b>
|
||||||
|
<P>
|
||||||
|
<b>Unemployment</b>
|
||||||
|
<P>
|
||||||
|
There are 2 main sources of unemployment data. An estimate of unemployment under the International Labour Office definition - ILO unemployment - is provided by the Labour Force Survey (LFS), a quarterly sample survey of households. The second measure of unemployment - the claimant count - is based on records of those claiming Jobseeker's Allowance and National Insurance Credits at Employment Service Offices. The Office for National Statistics announced on 3 February that (from April) its assessment of the labour market would give more weight than previously to the LFS, which is conducted according to internationally agreed definitions drawn up by the ILO.
|
||||||
|
<P>
|
||||||
|
ILO unemployment (not seasonally adjusted) in Scotland fell by 32,000 in the year to Autumn (September to November) 1997 to 185,000. The rate of unemployment fell by 1.4 percentage points to 7.4 per cent of the workforce. ILO unemployment in the UK fell by 379,000 in the year to Autumn 1997 to 1,919,000 or 6.6 per cent, 0.8 percentage points below the Scottish rate. Unemployment fell in every Government Office Region (GOR) of the UK. Four GORs - Merseyside, North East, London and Northern Ireland - have higher ILO unemployment rates than Scotland.
|
||||||
|
<P>
|
||||||
|
Claimant count unemployment (seasonally adjusted) in Scotland fell throughout 1997 but rose by 1,200 in January 1998 to 141,100, the first rise since April 1996. The rate of unemployment rose by 0.1 percentage point to 5.8 per cent of the workforce, 0.8 percentage points above the UK rate. Of the UK GORs, Merseyside, North East, and Northern Ireland have higher unemployment rates than Scotland, while London has the same rate.
|
||||||
|
<P>
|
||||||
|
The claimant count measure of unemployment in Scotland remains significantly lower than the ILO measure. The difference between the ILO measure and the claimant count measure<a name="note-12"></A><font size=-2><sup><a href="#notes">12</a></sup></font> in Autumn 1997 was 42,000, a rise of 7,000 on Autumn 1996.
|
||||||
|
<P>
|
||||||
|
In the July 1997 Budget, the Government set out a New Deal to help young people, the long term unemployed, lone parents and the disabled move from Welfare to Work. The New Deal for young claimants (aged 18-24) who have been unemployed for 6 months or more was launched in 12 "pathfinder" areas of the UK (including Tayside) in January and the programme will be launched nationally from April. The New Deal for long term unemployed adults (those aged 25 and over who have been unemployed for more than 2 years) will be launched in June.<a name="note-13"></A><font size=-2><sup><a href="#notes">13</a></sup></font>
|
||||||
|
<P>
|
||||||
|
Table 6 summarises the eligibility for the New Deal for these two groups in January 1998. It can be seen that, in Scotland there were 11,300 youth unemployed of over 6 months duration and 17,100 aged 25 and over who had been unemployed for 2 years or more in January 1998 (7.4 per cent and 11.3 per cent of total claimant unemployed, respectively). The total number in these 2 groups has fallen significantly over the last year - by 17,800 (38.5 per cent).
|
||||||
|
<P>
|
||||||
|
<b>Table 6: Claiment Count Unemployment for New Deal Target Groups</b>
|
||||||
|
<P>
|
||||||
|
<TABLE border>
|
||||||
|
<TR><td rowspan=2> </td><TD colspan=3><b>Youth (18-24) Unemployment, over 6 months duration</b></TD><TD colspan=3><b>Adult (25+) Unemployment, over 2 years duration</b></TD></TR>
|
||||||
|
<TR><TD><b>January 1997</b></TD><TD><b>January 1998</b></TD><TD><b>Percentage change</b></TD><TD><b>January 1997</b></TD><TD><b>January 1998</b></TD><TD><b>Percentage change</b></TD></TR>
|
||||||
|
<TR><TD>Scotland</TD><TD>18,100</TD><TD>11,300</TD><TD>-37.7</TD><TD>28,100</TD><TD>17,100</TD><TD>-39.1</TD></TR>
|
||||||
|
<TR><TD>Per cent of claimant count</TD><TD>9.8</TD><TD>7.4</TD><TD>..</TD><TD>15.2</TD><TD>11.3</TD><TD>..</TD></TR>
|
||||||
|
<TR><TD>UK</TD><TD>198,300</TD><TD>118,400</TD><TD>-40.3</TD><TD>357,000</TD><TD>216,300</TD><TD>-39.4</TD></TR>
|
||||||
|
<TR><TD>Scotland as a percentage of the UK</TD><TD>9.1</TD><TD>9.5</TD><TD>..</TD><TD>7.9</TD><TD>7.9</TD><TD>..</TD></TR>
|
||||||
|
</TABLE>
|
||||||
|
<BR>
|
||||||
|
Source: Office for National Statistics<P>
|
||||||
|
Note: 1. Percentages calculated with reference to unrounded figures.
|
||||||
|
<P>
|
||||||
|
<b>Employment</b>
|
||||||
|
<P>
|
||||||
|
There are two main official sources of quarterly employment data: the Workforce in Employment series, which is a survey of employers, and the Labour Force Survey.
|
||||||
|
<P>
|
||||||
|
An increase of 43,000 in total employment (not seasonally adjusted) in Scotland was recorded by the LFS over the year to Autumn 1997 to reach a new (Autumn) peak of 2,305,000. This was due to increases of 24,000 in the number of employees, 14,000 in the number of self-employed and 5,000 in the number of people either on government supported training and employment programmes or who were unpaid family workers. Given the fall of 32,000 in the level of ILO unemployment, the number of people classed as economically active increased by 10,000 in the year to Autumn 1997. Increases in total employment were evident in most UK GORs, falling only in the North East, Merseyside and Wales. In the UK as a whole, total employment increased by 456,000.
|
||||||
|
<P>
|
||||||
|
An increase of 23,000 in the civilian workforce (not seasonally adjusted) was recorded by the Workforce in Employment series over the year to September 1997 to reach a new peak of 2,277,000, (7,000 higher than the 1991 peak and 202,000 above the trough in 1983). This comprised increases of 19,000 in the number of self-employed and 6,000 in the number of employees (comprising increases across the service sector (14,000) and decreases in manufacturing (5,000) and other sectors (3,000)) over the year, partly offset by a fall of 2,000 in the number on work-related government training programmes. Increases in the civilian workforce were evident in all GB regions, except East Anglia and Yorkshire and Humberside. In Great Britain as a whole, the civilian workforce increased by 349,000.
|
||||||
|
<P>
|
||||||
|
The growth in the number of employees has been due to the increase in part-time employment.<a name="note-14"></A><font size=-2><sup><a href="#notes">14</a></sup></font> In the year to September 1997, part-time employment rose by 32,000 (17,000 males and 15,000 females), offset by a fall of 26,000 in full-time employment (24,000 males and 2,000 females). This is a continuation of a trend over the past few years in which part-time employment has increased - in each year since 1992 (data are available from 1991) - to a level 104,000 higher (46,000 males and 58,000 females) in 1997 than 5 years earlier. By contrast, full-time employment has fallen consistently and in September 1997 was 90,000 lower than 1992 levels (86,000 males and 4,000 females).
|
||||||
|
<P>
|
||||||
|
|
||||||
|
<a name="notes"></a>
|
||||||
|
<br>
|
||||||
|
<hr>
|
||||||
|
<font size=-2><sup><a href="#note-7">7</a></sup></font><font size=-1>Published in Economic Trends, February 1998.</font><br>
|
||||||
|
<font size=-2><sup><a href="#note-8">8</a></sup></font><font size=-1>GDP estimates of the Scottish regions measure the value of goods and services produced in an area; they do not measure the income of the residents in an area, as is the case for Government Office Regions/countries of the UK. There is a wide variation between areas in terms of size and population; in order to compare the economic performance of areas it is necessary to use an indicator such as GDP per head of population. Resident population is used as the denominator. The implication of using this in conjunction with the workplace-based GDP figures is that the productivity of urban areas into which workers commute will tend to be overstated by this indicator, while that of surrounding areas in which they live will be understated.</font><br>
|
||||||
|
<font size=-2><sup><a href="#note-9">9</a></sup></font><font size=-1>Input -Output Tables and Multipliers for Scotland, 1994, The Stationery Office. </font><br>
|
||||||
|
<font size=-2><sup><a href="#note-10">10</a></sup></font><font size=-1>It should be noted that the data presented by the SCDI for Scottish manufactured exports refer to gross output. They do not measure the level of (or changes in) the value-added component of Scottish manufactured exports (that is, the wages and profits accruing to domestic suppliers of labour and capital).</font><br>
|
||||||
|
<font size=-2><sup><a href="#note-11">11</a></sup></font><font size=-1>Electronics is classified by the SCDI as consisting of 4 industry groupings: Office Machinery, Electrical Machinery and Apparatus nec, Radio/TV/Communication Equipment and Apparatus and Medical, Precision and Optical Instruments, Watches and Clocks.</font><br>
|
||||||
|
<font size=-2><sup><a href="#note-12">12</a></sup></font><font size=-1>Average of September to November levels (not seasonally adjusted).</font><br>
|
||||||
|
<font size=-2><sup><a href="#note-13">13</a></sup></font><font size=-1>The New Deal for young people provides a period of advice and guidance -'the Gateway' - to find unsubsidised jobs. Thereafter, four options will be available: a subsidised job with an employer; a place on an Environment Task Force; a job in the voluntary sector; or full-time education or training. The first 3 options involve at least one day a week training and options 2 and 3 include top-ups to existing benefits. Long term unemployed adults under the New Deal will be able to benefit from two options: a subsidised job with an employer; or opportunities to study for up to 12 months in full-time employment-related courses designed to reach an accredited qualification. </font><br>
|
||||||
|
<font size=-2><sup><a href="#note-14">14</a></sup></font><font size=-1>Part-time employment is defined here as working less than 30 hours per week. </font>
|
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<hr>
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<a href="ecorev.htm"><img border=0 src="/img/prevblu.gif" alt="Back to previous section"></a>
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<a href="contents.htm"><img border=0 src="/img/ctntblu.gif" alt="Back to contents"></a>
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<a href="ecorev3.htm"><img border=0 src="/img/contblu.gif" alt="On to next section"></a>
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<hr>
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<table width=100%><tr><td><address><font size=-1><b>We welcome your <a href="/form/tsoform.htm">comments</a> on this site.
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</b></font></address></td>
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|
<td align=right><address><font size=-1>Prepared 13 March 1998</font></address></td></tr></table>
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Dataset Name:,"RT331601"
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Title:,"Area and population, 1996: Scotland"
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|
Description:,"Area and population, 1996: Scotland
|
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|
|
||||||
|
This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998.
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||||||
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|
||||||
|
"
|
||||||
|
Source:,"Office for National Statistics; General Register Office for Scotland"
|
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|
Time Frame:,"1996"
|
||||||
|
Geographic Coverage:,"United Kingdom"
|
||||||
|
Universe:,"UK population"
|
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|
Measure:,"various"
|
||||||
|
Units:,"See table"
|
||||||
|
Scalar:,"various"
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|
Formula:,"none"
|
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|
====================================
|
||||||
|
Table
|
||||||
|
,"Area (sq km)","Persons per sq km","Population (thousands) Males","Population (thousands) Females","Population (thousands) Total","Total population percentage change 1981-1996","Total period fertility rate (TPFR)<1>","Standardised mortality ratio (UK=100) (SMR)<2>","Percentage of population aged under 5","Percentage of population aged 5-15","Percentage of population aged 16 up to pension age<3>","Percentage of population of pension age or over<4>",
|
||||||
|
|
||||||
|
"United Kingdom","242910.00","242.00","28856.00","29946.00","58801.00","4.30","1.72","100.00","6.40","14.20","61.30","18.10",
|
||||||
|
"Scotland","78133.00","66.00","2486.00","2642.00","5128.00","-1.00","1.55","116.00","6.10","13.90","63.10","17.80",
|
||||||
|
"Aberdeen City","186.00","1169.00","106.00","111.00","217.00","2.20","1.35","105.00","5.80","12.30","65.80","17.10",
|
||||||
|
"Aberdeenshire","6318.00","36.00","113.00","114.00","227.00","20.40","1.64","97.00","6.50","15.30","63.80","15.30",
|
||||||
|
"Angus","2181.00","51.00","54.00","57.00","111.00","4.90","1.67","113.00","6.00","13.90","61.60","19.40",
|
||||||
|
"Argyll and Bute","6930.00","13.00","45.00","46.00","91.00","-0.10","1.70","109.00","5.50","13.60","61.10","20.80",
|
||||||
|
"Clackmannanshire","157.00","312.00","24.00","25.00","49.00","1.20","1.76","115.00","6.60","14.60","63.00","16.70",
|
||||||
|
"Dumfries and Galloway","6439.00","23.00","72.00","76.00","148.00","1.40","1.78","107.00","5.90","13.80","60.30","21.20",
|
||||||
|
"Dundee City","65.00","2306.00","72.00","79.00","150.00","-11.40","1.57","118.00","5.90","13.30","62.00","19.90",
|
||||||
|
"East Ayrshire","1252.00","98.00","59.00","63.00","122.00","-3.90","1.64","114.00","6.30","14.50","61.90","18.30",
|
||||||
|
"East Dunbartonshire","172.00","645.00","54.00","57.00","111.00","1.00","1.56","102.00","5.80","14.20","64.50","16.50",
|
||||||
|
"East Lothian","678.00","130.00","43.00","45.00","88.00","9.20","1.77","112.00","6.40","13.70","61.50","19.50",
|
||||||
|
"East Renfrewshire","173.00","510.00","43.00","45.00","88.00","9.80","1.67","96.00","6.20","14.60","63.00","17.10",
|
||||||
|
"Edinburgh, City of","262.00","1711.00","217.00","232.00","449.00","0.60","1.34","111.00","5.70","11.60","65.80","17.90",
|
||||||
|
"Eilean Siar (Western Isles)","3134.00","9.00","14.00","15.00","29.00","-8.50","1.65","117.00","5.50","15.10","59.80","20.70",
|
||||||
|
"Falkirk","299.00","478.00","69.00","74.00","143.00","-1.50","1.58","121.00","6.20","13.70","63.80","17.40",
|
||||||
|
"Fife","1323.00","264.00","169.00","180.00","349.00","2.30","1.55","109.00","6.00","14.40","62.30","18.30",
|
||||||
|
"Glasgow City","175.00","3522.00","294.00","322.00","616.00","-13.50","1.48","137.00","6.30","13.30","63.20","18.10",
|
||||||
|
"Highland","25784.00","8.00","102.00","106.00","209.00","7.10","1.77","109.00","6.20","14.90","61.80","18.10",
|
||||||
|
"Inverclyde","162.00","538.00","42.00","45.00","87.00","-13.90","1.66","138.00","6.20","14.60","61.70","18.60",
|
||||||
|
"Midlothian","356.00","225.00","39.00","41.00","80.00","-4.20","1.61","119.00","6.10","14.30","64.20","16.40",
|
||||||
|
"Moray","2238.00","39.00","43.00","44.00","87.00","3.60","1.76","108.00","6.60","14.60","61.80","18.00",
|
||||||
|
"North Ayrshire","884.00","158.00","67.00","72.00","140.00","1.60","1.63","115.00","6.20","15.00","62.20","17.60",
|
||||||
|
"North Lanarkshire","474.00","688.00","158.00","168.00","326.00","-4.60","1.66","126.00","6.40","14.90","63.80","15.90",
|
||||||
|
"Orkney Islands","992.00","20.00","10.00","10.00","20.00","3.20","1.78","106.00","6.00","15.30","61.20","18.50",
|
||||||
|
"Perth and Kinross","5311.00","25.00","64.00","69.00","133.00","8.80","1.61","103.00","5.60","13.80","60.70","20.90",
|
||||||
|
"Renfrewshire","261.00","683.00","86.00","92.00","179.00","-3.50","1.59","125.00","6.30","14.10","63.70","17.00",
|
||||||
|
"Scottish Borders, The","4734.00","22.00","51.00","55.00","106.00","4.80","1.67","100.00","5.80","13.30","60.20","21.80",
|
||||||
|
"Shetland Islands","1438.00","16.00","12.00","11.00","23.00","-12.60","1.77","117.00","7.00","15.90","62.90","14.90",
|
||||||
|
"South Ayrshire","1202.00","95.00","55.00","60.00","115.00","1.30","1.55","105.00","5.50","13.70","61.10","20.90",
|
||||||
|
"South Lanarkshire","1771.00","174.00","149.00","159.00","307.00","-0.80","1.55","125.00","6.30","14.50","63.80","16.50",
|
||||||
|
"Stirling","2196.00","38.00","40.00","43.00","83.00","3.10","1.55","110.00","5.70","13.60","63.80","17.90",
|
||||||
|
"West Dunbartonshire","162.00","590.00","46.00","50.00","96.00","-9.50","1.70","130.00","6.40","15.30","61.40","17.80",
|
||||||
|
"West Lothian","425.00","355.00","74.00","77.00","151.00","8.30","1.63","126.00","6.80","15.10","65.80","13.20",
|
||||||
|
====================================
|
||||||
|
Footnotes
|
||||||
|
"1 - The total period fertility rate (TPFR) is the average number of children which would be born to a woman if the current pattern of fertility persisted throughout her child-bearing years."
|
||||||
|
"2 - Adjusted for the age structure of the population."
|
||||||
|
"3 - Pension age is 65 for males and 60 for females."
|
||||||
|
"4 - New Councils for Scotland"
|
||||||
|
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|
|||||||
|
"PrivateHousingStarts" "PublicHousingStarts" "StockofDwellings" "Households" "LocalAuthorityRent" "CouncilTax" "Areas" "Density" "Males" "Females" "Population" "PopulationChange" "FertilityRate" "StdMortalityRatio" "PercentageUnder5" "Percentage5to15" "Percentage16topension" "PercentageOverPensionage" "InNursery" "InPreschool" "PrimaryPTRatio" "SecondaryPTRatio" "PostCompulsory" "NoGrade" "Grades" "InJobTraining" "Birthsper1000" "Deathsper1000" "PeriMortality" "InfantMortality" "PerBirthsOut" "Active" "TotalEmployment" "PerMfgEmployment" "PerUnemployment" "TotalClaimants" "PerClaimantFemale" "PerClaimLongT" "MeanWeekSal" "GDP" "Var.41" "Var.42"
|
||||||
|
"Aberdeen_City" 1142 136 100 96.8 27.5 712 186 1169 106 111 217 2.2 1.35 105 5.8 12.3 65.8 17.1 126 50 19.9 12.9 113 1.9 53 15.4 11 10.4 7.7 5.7 35 82.4 113 14.3 4.9 3.6 21 17.6 404.8 13566 71.8 60.3
|
||||||
|
"Aberdeenshire" 533 90 92 87.1 29.7 643 6318 36 113 114 227 20.4 1.64 97 6.5 15.3 63.8 15.3 34.4 34 18.6 13.8 81 9.2 58.2 11.4 11.3 9 9 3.8 24 80.2 112 14.5 NA 2.8 26.5 16.3 330.9 13566 63.9 52.3
|
||||||
|
"Angus" 272 167 48 46.2 23.5 679 2181 51 54 57 111 4.9 1.67 113 6 13.9 61.6 19.4 70.2 38 19.1 13 89 NA 62 NA 11 13.2 5.6 3.2 33 86.3 59 16.9 NA 3.2 28.3 23.3 320 9611 64.7 53.4
|
||||||
|
"Argyll_and_Bute" 300 266 43 37.7 35 801 6930 13 45 46 91 -0.1 1.7 109 5.5 13.6 61.1 20.8 38.8 13 17.3 12.4 81 8.6 54.1 NA 10.5 13.8 8.6 7 33 80.4 41 NA 11.9 2.9 27.1 22.8 305.2 9483 67.3 57
|
||||||
|
"Clackmannanshire" 125 37 21 20 29.6 753 157 312 24 25 49 1.2 1.76 115 6.6 14.6 63 16.7 90.9 47 21.2 13.4 78 NA 63.6 NA 12.3 11.3 10.4 6.3 40 64.7 17 NA NA 1.6 22 27.5 NA 9265 80 68.7
|
||||||
|
"Dumfries_and_Galloway" 388 159 66 62.3 32.7 714 6439 23 72 76 148 1.4 1.78 107 5.9 13.8 60.3 21.2 21.3 44 18.9 12.7 93 2.7 60.2 NA 10.9 12.8 8.8 7.8 34 79 67 17.7 NA 4.4 24.3 23.4 300.2 9555 60.7 48.8
|
||||||
|
"Dundee_City" 182 151 72 67.5 36.8 920 65 2306 72 79 150 -11.4 1.57 118 5.9 13.3 62 19.9 114.3 59 18 12.2 117 0.6 46.9 NA 11.5 13.1 8.6 6.8 51 72.2 60 14.6 9.3 6.2 21.2 26.3 327.4 9611 76 65.5
|
||||||
|
"East_Ayrshire" 262 30 51 50.1 26.9 779 1252 98 59 63 122 -3.9 1.64 114 6.3 14.5 61.9 18.3 43.4 43 21 13.5 89 6.4 50.2 NA 11.4 11.6 12.3 6.5 40 75.2 50 23.1 14.2 4.5 20.5 28.9 307.6 9483 81.1 70.5
|
||||||
|
"East_Dunbartonshire" 236 6 42 41.3 29.6 771 172 645 54 57 111 1 1.56 102 5.8 14.2 64.5 16.5 76.2 11 22.2 13.9 94 NA 71.2 NA 10.5 9.2 8.1 7.2 19 81.1 53 NA NA 2.2 23.2 17.3 329.2 9483 69.8 59.1
|
||||||
|
"East_Lothian" 469 165 38 36.3 28.8 724 678 130 43 45 88 9.2 1.77 112 6.4 13.7 61.5 19.5 48.6 56 20.6 13.4 66 12.7 46 NA 12.3 12.6 7.6 5.2 29 80.3 41 NA NA 1.7 20.5 16.3 310.3 12656 74.2 62.7
|
||||||
|
"East_Renfrewshire" 295 96 34 33.1 28.9 682 173 510 43 45 88 9.8 1.67 96 6.2 14.6 63 17.1 146 33 22.3 14 91 NA 78.8 NA 11.5 9.5 7.4 6.2 19 83 42 16.8 NA 1.4 23.8 20.9 NA 9483 61.7 51.6
|
||||||
|
"Edinburgh_City" 1496 525 206 198.2 43.8 837 262 1711 217 232 449 0.6 1.34 111 5.7 11.6 65.8 17.9 132.3 50 20.7 13.4 109 2.3 56.7 14.7 11.4 11.7 8.1 6.4 33 74.5 207 10.3 6.6 11.1 22.1 20.4 362.8 12656 71.9 62
|
||||||
|
"Eilean_Siar_(Western_Isles)" 75 10 13 11.6 36.5 599 3134 9 14 15 29 -8.5 1.65 117 5.5 15.1 59.8 20.7 12.6 NA 13 9.5 102 3.2 60.9 NA 9.7 14.9 11.2 5.7 19 83.8 15 NA NA 1.4 19.9 23.8 NA 8298 79.4 68.4
|
||||||
|
"Falkirk" 651 66 61 59.1 29.8 680 299 478 69 74 143 -1.5 1.58 121 6.2 13.7 63.8 17.4 81.8 40 21.3 13.6 91 4 49.4 NA 11.7 11.7 7.9 4.8 34 77.6 66 23.4 NA 4.5 21.5 19.9 335.6 9265 80 69.2
|
||||||
|
"Fife" 202 251 152 145.6 30.3 747 1323 264 169 180 349 2.3 1.55 109 6 14.4 62.3 18.3 20.2 51 19.1 13.4 106 5.3 52.1 13.5 11 11.4 8.7 7.1 37 77.9 147 21.7 9.3 11.1 22.5 22.7 325.2 8314 76.1 64.7
|
||||||
|
"Glasgow_City" 1884 1056 286 271.9 40.4 982 175 3522 294 322 616 -13.5 1.48 137 6.3 13.3 63.2 18.1 99.8 53 19.3 12.4 88 12.7 41.9 15.3 12.5 14 11.1 6.9 49 65.3 210 14.2 15.2 26.9 19.4 29.5 341.5 9483 83.6 75
|
||||||
|
"Highland" 664 161 95 85.8 38.5 719 25784 8 102 106 209 7.1 1.77 109 6.2 14.9 61.8 18.1 41.5 19 17.3 11.8 94 NA 60 NA 11.4 11.4 8.3 6.5 34 80.9 100 12.9 9.3 7.9 25.9 20.9 296.2 8298 72.6 62.1
|
||||||
|
"Inverclyde" 291 126 39 38 34.6 831 162 538 42 45 87 -13.9 1.66 138 6.2 14.6 61.7 18.6 105.8 27 21.4 13.6 95 NA 56.2 NA 11.7 14.5 11.5 8 45 80.2 39 26.5 NA 2.5 18.5 12.7 323.4 9483 78 67.2
|
||||||
|
"Midlothian" 362 61 32 30.8 25.2 858 356 225 39 41 80 -4.2 1.61 119 6.1 14.3 64.2 16.4 49 54 19.9 13.6 77 3.8 53 NA 11.2 10.7 10.8 6 35 84.8 39 NA NA 1.6 19.4 13.7 309 12656 79.9 67.7
|
||||||
|
"Moray" 327 0 37 34.9 28 652 2238 39 43 44 87 3.6 1.76 108 6.6 14.6 61.8 18 21.6 31 18.9 12.2 91 8 54.3 NA 12.4 11 9.8 7.4 26 86.4 43 14.3 NA 2.2 27.2 15.4 285 13566 67.2 52.7
|
||||||
|
"North_Ayrshire" 344 157 60 57.7 30.2 718 884 158 67 72 140 1.6 1.63 115 6.2 15 62.2 17.6 115.3 23 21.2 13.4 72 10.5 45.6 NA 11.3 11.8 11.6 6.9 42 73.5 58 27.4 9.1 5.1 23.7 19.2 317.8 9483 76.3 65.7
|
||||||
|
"North_Lanarkshire" 1557 175 130 128.5 31.3 787 474 688 158 168 326 -4.6 1.66 126 6.4 14.9 63.8 15.9 64.4 25 20.2 13.4 94 2.3 47.5 11.8 12.5 11.1 11.6 8.5 38 74.7 133 21.2 12.4 10.7 20.8 20.1 336.7 9483 82.6 72.2
|
||||||
|
"Orkney_Islands" 0 6 9 8.1 33.8 515 992 20 10 10 20 3.2 1.78 106 6 15.3 61.2 18.5 31.8 52 15.1 10.9 97 0 69.3 NA 10.9 11.6 7.5 1.4 30 87.8 10 NA NA 0.4 26.8 24.4 NA 8298 57.3 47.4
|
||||||
|
"Perth_and_Kinross" 448 147 59 55 28.2 732 5311 25 64 69 133 8.8 1.61 103 5.6 13.8 60.7 20.9 93.2 45 18.7 12.5 78 10.2 53.9 NA 10.5 12.6 9.8 5.9 29 86.6 66 11.3 NA 2.8 23 17.8 NA 9611 61.7 51.3
|
||||||
|
"Renfrewshire" 732 66 77 75.1 32.5 783 261 683 86 92 179 -3.5 1.59 125 6.3 14.1 63.7 17 119.6 31 22 13.7 103 NA 55.9 15.5 11.9 11.6 8 4.5 39 78.5 80 20.1 11.3 5.5 20.5 23.3 336.1 9483 79 63.6
|
||||||
|
"Scottish_Borders_The" 245 98 49 44.9 29.7 612 4734 22 51 55 106 4.8 1.67 100 5.8 13.3 60.2 21.8 56.9 22 18.5 12.1 92 1.3 61.7 NA 10.7 12.8 8 4.9 28 80.6 49 20.6 NA 2.1 23.7 12.3 303.5 9033 62.8 50.7
|
||||||
|
"Shetland_Islands" 131 21 10 8.9 36.1 486 1438 16 12 11 23 -12.6 1.77 117 7 15.9 62.9 14.9 14.8 42 12.7 8.1 79 NA 73.6 NA 11.7 10.9 9.9 6.5 28 84.8 11 NA NA 0.4 23.2 14.2 NA 8298 62.4 51.6
|
||||||
|
"South_Ayrshire" 182 80 49 47.6 30.7 765 1202 95 55 60 115 1.3 1.55 105 5.5 13.7 61.1 20.9 70.4 36 21 13.6 99 NA 61.9 NA 10.1 12.7 6.2 4.3 33 79.2 48 22.9 10.3 3.6 23.6 23.4 346.2 9483 66.9 56.2
|
||||||
|
"South_Lanarkshire" 488 98 124 122.3 35.3 793 1771 174 149 159 307 -0.8 1.55 125 6.3 14.5 63.8 16.5 103.9 15 20.7 13.7 92 3.6 51.5 13.7 11.5 11.3 9.2 5.1 33 78.4 146 22 8.3 8.2 21.7 21.8 319.1 9483 77.8 67.6
|
||||||
|
"Stirling" 341 66 34 33.1 33.6 776 2196 38 40 43 83 3.1 1.55 110 5.7 13.6 63.8 17.9 200.5 46 19.5 13.3 81 2.9 61.4 NA 11.1 11.8 7.7 4.9 33 77.2 37 NA NA 2.1 23 19.5 346.6 9265 68.5 58.9
|
||||||
|
"West_Dunbartonshire" 193 139 42 40.4 33.4 978 162 590 46 50 96 -9.5 1.7 130 6.4 15.3 61.4 17.8 81.9 47 20.3 14 108 NA 52.5 NA 12.5 12.7 11.7 8.7 42 71.5 36 NA 13.6 4.2 19.3 27.3 319 9483 84.7 74.7
|
||||||
|
"West_Lothian" 942 156 61 60.3 28.3 792 425 355 74 77 151 8.3 1.63 126 6.8 15.1 65.8 13.2 53.6 51 20.3 13.5 80 6.3 46.8 NA 13.1 9.5 8.7 4.6 33 82.2 78 32.6 NA 3.5 21.2 10.2 335.5 12656 79.6 67.3
|
||||||
|
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|
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|
|||||||
|
"PrivateHousingStarts" "PublicHousingStarts" "StockofDwellings" "Households" "LocalAuthorityRent" "CouncilTax" "Areas" "Density" "Males" "Females" "Population" "PopulationChange" "FertilityRate" "StdMortalityRatio" "PercentageUnder5" "Percentage5to15" "Percentage16topension" "PercentageOverPensionage" "InNursery" "InPreschool" "PrimaryPTRatio" "SecondaryPTRatio" "PostCompulsory" "NoGrade" "Grades" "InJobTraining" "Birthsper1000" "Deathsper1000" "PeriMortality" "InfantMortality" "PerBirthsOut" "Active" "TotalEmployment" "PerMfgEmployment" "PerUnemployment" "TotalClaimants" "PerClaimantFemale" "PerClaimLongT" "MeanWeekSal" "GDP" "Var.41" "Var.42"
|
||||||
|
"Aberdeen_City" 1142 136 100 96.8 27.5 712 186 1169 106 111 217 2.2 1.35 105 5.8 12.3 65.8 17.1 126 50 19.9 12.9 113 1.9 53 15.4 11 10.4 7.7 5.7 35 82.4 113 14.3 4.9 3.6 21 17.6 404.8 13566 71.8 60.3
|
||||||
|
"Aberdeenshire" 533 90 92 87.1 29.7 643 6318 36 113 114 227 20.4 1.64 97 6.5 15.3 63.8 15.3 34.4 34 18.6 13.8 81 9.2 58.2 11.4 11.3 9 9 3.8 24 80.2 112 14.5 NA 2.8 26.5 16.3 330.9 13566 63.9 52.3
|
||||||
|
"Angus" 272 167 48 46.2 23.5 679 2181 51 54 57 111 4.9 1.67 113 6 13.9 61.6 19.4 70.2 38 19.1 13 89 NA 62 NA 11 13.2 5.6 3.2 33 86.3 59 16.9 NA 3.2 28.3 23.3 320 9611 64.7 53.4
|
||||||
|
"Argyll_and_Bute" 300 266 43 37.7 35 801 6930 13 45 46 91 -0.1 1.7 109 5.5 13.6 61.1 20.8 38.8 13 17.3 12.4 81 8.6 54.1 NA 10.5 13.8 8.6 7 33 80.4 41 NA 11.9 2.9 27.1 22.8 305.2 9483 67.3 57
|
||||||
|
"Clackmannanshire" 125 37 21 20 29.6 753 157 312 24 25 49 1.2 1.76 115 6.6 14.6 63 16.7 90.9 47 21.2 13.4 78 NA 63.6 NA 12.3 11.3 10.4 6.3 40 64.7 17 NA NA 1.6 22 27.5 NA 9265 80 68.7
|
||||||
|
"Dumfries_and_Galloway" 388 159 66 62.3 32.7 714 6439 23 72 76 148 1.4 1.78 107 5.9 13.8 60.3 21.2 21.3 44 18.9 12.7 93 2.7 60.2 NA 10.9 12.8 8.8 7.8 34 79 67 17.7 NA 4.4 24.3 23.4 300.2 9555 60.7 48.8
|
||||||
|
"Dundee_City" 182 151 72 67.5 36.8 920 65 2306 72 79 150 -11.4 1.57 118 5.9 13.3 62 19.9 114.3 59 18 12.2 117 0.6 46.9 NA 11.5 13.1 8.6 6.8 51 72.2 60 14.6 9.3 6.2 21.2 26.3 327.4 9611 76 65.5
|
||||||
|
"East_Ayrshire" 262 30 51 50.1 26.9 779 1252 98 59 63 122 -3.9 1.64 114 6.3 14.5 61.9 18.3 43.4 43 21 13.5 89 6.4 50.2 NA 11.4 11.6 12.3 6.5 40 75.2 50 23.1 14.2 4.5 20.5 28.9 307.6 9483 81.1 70.5
|
||||||
|
"East_Dunbartonshire" 236 6 42 41.3 29.6 771 172 645 54 57 111 1 1.56 102 5.8 14.2 64.5 16.5 76.2 11 22.2 13.9 94 NA 71.2 NA 10.5 9.2 8.1 7.2 19 81.1 53 NA NA 2.2 23.2 17.3 329.2 9483 69.8 59.1
|
||||||
|
"East_Lothian" 469 165 38 36.3 28.8 724 678 130 43 45 88 9.2 1.77 112 6.4 13.7 61.5 19.5 48.6 56 20.6 13.4 66 12.7 46 NA 12.3 12.6 7.6 5.2 29 80.3 41 NA NA 1.7 20.5 16.3 310.3 12656 74.2 62.7
|
||||||
|
"East_Renfrewshire" 295 96 34 33.1 28.9 682 173 510 43 45 88 9.8 1.67 96 6.2 14.6 63 17.1 146 33 22.3 14 91 NA 78.8 NA 11.5 9.5 7.4 6.2 19 83 42 16.8 NA 1.4 23.8 20.9 NA 9483 61.7 51.6
|
||||||
|
"Edinburgh_City" 1496 525 206 198.2 43.8 837 262 1711 217 232 449 0.6 1.34 111 5.7 11.6 65.8 17.9 132.3 50 20.7 13.4 109 2.3 56.7 14.7 11.4 11.7 8.1 6.4 33 74.5 207 10.3 6.6 11.1 22.1 20.4 362.8 12656 71.9 62
|
||||||
|
"Eilean_Siar_(Western_Isles)" 75 10 13 11.6 36.5 599 3134 9 14 15 29 -8.5 1.65 117 5.5 15.1 59.8 20.7 12.6 NA 13 9.5 102 3.2 60.9 NA 9.7 14.9 11.2 5.7 19 83.8 15 NA NA 1.4 19.9 23.8 NA 8298 79.4 68.4
|
||||||
|
"Falkirk" 651 66 61 59.1 29.8 680 299 478 69 74 143 -1.5 1.58 121 6.2 13.7 63.8 17.4 81.8 40 21.3 13.6 91 4 49.4 NA 11.7 11.7 7.9 4.8 34 77.6 66 23.4 NA 4.5 21.5 19.9 335.6 9265 80 69.2
|
||||||
|
"Fife" 202 251 152 145.6 30.3 747 1323 264 169 180 349 2.3 1.55 109 6 14.4 62.3 18.3 20.2 51 19.1 13.4 106 5.3 52.1 13.5 11 11.4 8.7 7.1 37 77.9 147 21.7 9.3 11.1 22.5 22.7 325.2 8314 76.1 64.7
|
||||||
|
"Glasgow_City" 1884 1056 286 271.9 40.4 982 175 3522 294 322 616 -13.5 1.48 137 6.3 13.3 63.2 18.1 99.8 53 19.3 12.4 88 12.7 41.9 15.3 12.5 14 11.1 6.9 49 65.3 210 14.2 15.2 26.9 19.4 29.5 341.5 9483 83.6 75
|
||||||
|
"Highland" 664 161 95 85.8 38.5 719 25784 8 102 106 209 7.1 1.77 109 6.2 14.9 61.8 18.1 41.5 19 17.3 11.8 94 NA 60 NA 11.4 11.4 8.3 6.5 34 80.9 100 12.9 9.3 7.9 25.9 20.9 296.2 8298 72.6 62.1
|
||||||
|
"Inverclyde" 291 126 39 38 34.6 831 162 538 42 45 87 -13.9 1.66 138 6.2 14.6 61.7 18.6 105.8 27 21.4 13.6 95 NA 56.2 NA 11.7 14.5 11.5 8 45 80.2 39 26.5 NA 2.5 18.5 12.7 323.4 9483 78 67.2
|
||||||
|
"Midlothian" 362 61 32 30.8 25.2 858 356 225 39 41 80 -4.2 1.61 119 6.1 14.3 64.2 16.4 49 54 19.9 13.6 77 3.8 53 NA 11.2 10.7 10.8 6 35 84.8 39 NA NA 1.6 19.4 13.7 309 12656 79.9 67.7
|
||||||
|
"Moray" 327 0 37 34.9 28 652 2238 39 43 44 87 3.6 1.76 108 6.6 14.6 61.8 18 21.6 31 18.9 12.2 91 8 54.3 NA 12.4 11 9.8 7.4 26 86.4 43 14.3 NA 2.2 27.2 15.4 285 13566 67.2 52.7
|
||||||
|
"North_Ayrshire" 344 157 60 57.7 30.2 718 884 158 67 72 140 1.6 1.63 115 6.2 15 62.2 17.6 115.3 23 21.2 13.4 72 10.5 45.6 NA 11.3 11.8 11.6 6.9 42 73.5 58 27.4 9.1 5.1 23.7 19.2 317.8 9483 76.3 65.7
|
||||||
|
"North_Lanarkshire" 1557 175 130 128.5 31.3 787 474 688 158 168 326 -4.6 1.66 126 6.4 14.9 63.8 15.9 64.4 25 20.2 13.4 94 2.3 47.5 11.8 12.5 11.1 11.6 8.5 38 74.7 133 21.2 12.4 10.7 20.8 20.1 336.7 9483 82.6 72.2
|
||||||
|
"Orkney_Islands" 0 6 9 8.1 33.8 515 992 20 10 10 20 3.2 1.78 106 6 15.3 61.2 18.5 31.8 52 15.1 10.9 97 0 69.3 NA 10.9 11.6 7.5 1.4 30 87.8 10 NA NA 0.4 26.8 24.4 NA 8298 57.3 47.4
|
||||||
|
"Perth_and_Kinross" 448 147 59 55 28.2 732 5311 25 64 69 133 8.8 1.61 103 5.6 13.8 60.7 20.9 93.2 45 18.7 12.5 78 10.2 53.9 NA 10.5 12.6 9.8 5.9 29 86.6 66 11.3 NA 2.8 23 17.8 NA 9611 61.7 51.3
|
||||||
|
"Renfrewshire" 732 66 77 75.1 32.5 783 261 683 86 92 179 -3.5 1.59 125 6.3 14.1 63.7 17 119.6 31 22 13.7 103 NA 55.9 15.5 11.9 11.6 8 4.5 39 78.5 80 20.1 11.3 5.5 20.5 23.3 336.1 9483 79 63.6
|
||||||
|
"Scottish_Borders_The" 245 98 49 44.9 29.7 612 4734 22 51 55 106 4.8 1.67 100 5.8 13.3 60.2 21.8 56.9 22 18.5 12.1 92 1.3 61.7 NA 10.7 12.8 8 4.9 28 80.6 49 20.6 NA 2.1 23.7 12.3 303.5 9033 62.8 50.7
|
||||||
|
"Shetland_Islands" 131 21 10 8.9 36.1 486 1438 16 12 11 23 -12.6 1.77 117 7 15.9 62.9 14.9 14.8 42 12.7 8.1 79 NA 73.6 NA 11.7 10.9 9.9 6.5 28 84.8 11 NA NA 0.4 23.2 14.2 NA 8298 62.4 51.6
|
||||||
|
"South_Ayrshire" 182 80 49 47.6 30.7 765 1202 95 55 60 115 1.3 1.55 105 5.5 13.7 61.1 20.9 70.4 36 21 13.6 99 NA 61.9 NA 10.1 12.7 6.2 4.3 33 79.2 48 22.9 10.3 3.6 23.6 23.4 346.2 9483 66.9 56.2
|
||||||
|
"South_Lanarkshire" 488 98 124 122.3 35.3 793 1771 174 149 159 307 -0.8 1.55 125 6.3 14.5 63.8 16.5 103.9 15 20.7 13.7 92 3.6 51.5 13.7 11.5 11.3 9.2 5.1 33 78.4 146 22 8.3 8.2 21.7 21.8 319.1 9483 77.8 67.6
|
||||||
|
"Stirling" 341 66 34 33.1 33.6 776 2196 38 40 43 83 3.1 1.55 110 5.7 13.6 63.8 17.9 200.5 46 19.5 13.3 81 2.9 61.4 NA 11.1 11.8 7.7 4.9 33 77.2 37 NA NA 2.1 23 19.5 346.6 9265 68.5 58.9
|
||||||
|
"West_Dunbartonshire" 193 139 42 40.4 33.4 978 162 590 46 50 96 -9.5 1.7 130 6.4 15.3 61.4 17.8 81.9 47 20.3 14 108 NA 52.5 NA 12.5 12.7 11.7 8.7 42 71.5 36 NA 13.6 4.2 19.3 27.3 319 9483 84.7 74.7
|
||||||
|
"West_Lothian" 942 156 61 60.3 28.3 792 425 355 74 77 151 8.3 1.63 126 6.8 15.1 65.8 13.2 53.6 51 20.3 13.5 80 6.3 46.8 NA 13.1 9.5 8.7 4.6 33 82.2 78 32.6 NA 3.5 21.2 10.2 335.5 12656 79.6 67.3
|
||||||
@ -0,0 +1 @@
|
|||||||
|
from data import *
|
||||||
@ -0,0 +1,67 @@
|
|||||||
|
"""Spector and Mazzeo (1980) - Program Effectiveness Data"""
|
||||||
|
|
||||||
|
__docformat__ = 'restructuredtext'
|
||||||
|
|
||||||
|
COPYRIGHT = """Used with express permission of the original author, who
|
||||||
|
retains all rights. """
|
||||||
|
TITLE = __doc__
|
||||||
|
SOURCE = """
|
||||||
|
http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
|
||||||
|
|
||||||
|
The raw data was downloaded from Bill Greene's Econometric Analysis web site,
|
||||||
|
though permission was obtained from the original researcher, Dr. Lee Spector,
|
||||||
|
Professor of Economics, Ball State University."""
|
||||||
|
|
||||||
|
DESCRSHORT = """Experimental data on the effectiveness of the personalized
|
||||||
|
system of instruction (PSI) program"""
|
||||||
|
|
||||||
|
DESCRLONG = DESCRSHORT
|
||||||
|
|
||||||
|
NOTE = """
|
||||||
|
Number of Observations - 32
|
||||||
|
|
||||||
|
Number of Variables - 4
|
||||||
|
|
||||||
|
Variable name definitions::
|
||||||
|
|
||||||
|
Grade - binary variable indicating whether or not a student's grade
|
||||||
|
improved. 1 indicates an improvement.
|
||||||
|
TUCE - Test score on economics test
|
||||||
|
PSI - participation in program
|
||||||
|
GPA - Student's grade point average
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import scikits.statsmodels.tools.datautils as du
|
||||||
|
from os.path import dirname, abspath
|
||||||
|
|
||||||
|
def load():
|
||||||
|
"""
|
||||||
|
Load the Spector dataset and returns a Dataset class instance.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray(data, endog_idx=3, dtype=float)
|
||||||
|
|
||||||
|
def load_pandas():
|
||||||
|
"""
|
||||||
|
Load the Spector dataset and returns a Dataset class instance.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
Dataset instance:
|
||||||
|
See DATASET_PROPOSAL.txt for more information.
|
||||||
|
"""
|
||||||
|
data = _get_data()
|
||||||
|
return du.process_recarray_pandas(data, endog_idx=3, dtype=float)
|
||||||
|
|
||||||
|
def _get_data():
|
||||||
|
filepath = dirname(abspath(__file__))
|
||||||
|
##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####
|
||||||
|
data = np.recfromtxt(open(filepath + '/spector.csv',"rb"), delimiter=" ",
|
||||||
|
names=True, dtype=float, usecols=(1,2,3,4))
|
||||||
|
return data
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue