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Deleted statsmodels

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Per.Andreas.Brodtkorb 10 years ago
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*.py[oc]
# setup.py working directory
build
# setup.py dist directory
./dist
# Editor temporary/working/backup files
*$
.*.sw[nop]
.sw[nop]
*~
[#]*#
.#*
*.bak
*.tmp
*.tgz
*.rej
*.org
.project
*.diff
.settings/
*.svn/
*.log.py
# Egg metadata
./*.egg-info
# The shelf plugin uses this dir
./.shelf
# Mac droppings
.DS_Store
help

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statsmodels/.coveragerc
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# .coveragerc to control coverage.py
[run]
branch = False
[report]
# Regexes for lines to exclude from consideration
exclude_lines =
# Have to re-enable the standard pragma
pragma: no cover
# Don't complain about missing debug-only code:
def __repr__
if self\.debug
# Don't complain if tests don't hit defensive assertion code:
raise AssertionError
raise NotImplementedError
# Don't complain if non-runnable code isn't run:
if 0:
if __name__ == .__main__.:
ignore_errors = False
[html]
directory = coverage_html_report

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* text=auto

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*.py[oc]
# setup.py working directory
build
# setup.py dist directory
./dist
# repository directories for bzr-git
.bzr
.git
marks.git
marks.bzr
# Editor temporary/working/backup files
*$
.*.sw[nop]
.sw[nop]
*~
[#]*#
.#*
*.bak
*.tmp
*.tgz
*.rej
*.org
.project
*.diff
.settings/
*.svn/
*.log.py
# Egg metadata
./*.egg-info
# The shelf plugin uses this dir
./.shelf
# Mac droppings
.DS_Store
help
# Project specific
scikits/statsmodels/version.py

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statsmodels/CHANGES.txt
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Release History
===============
trunk for 0.4.0
---------------
* tools.tools.ECDF -> distributions.ECDF
* tools.tools.monotone_fn_inverter -> distributions.monotone_fn_inverter
* tools.tools.StepFunction -> distributions.StepFunction
0.3.1
-----
* Removed academic-only WFS dataset.
* Fix easy_install issue on Windows.
0.3.0
-----
*Changes that break backwards compatibility*
Added api.py for importing. So the new convention for importing is::
import scikits.statsmodels.api as sm
Importing from modules directly now avoids unnecessary imports and increases
the import speed if a library or user only needs specific functions.
* sandbox/output.py -> iolib/table.py
* lib/io.py -> iolib/foreign.py (Now contains Stata .dta format reader)
* family -> families
* families.links.inverse -> families.links.inverse_power
* Datasets' Load class is now load function.
* regression.py -> regression/linear_model.py
* discretemod.py -> discrete/discrete_model.py
* rlm.py -> robust/robust_linear_model.py
* glm.py -> genmod/generalized_linear_model.py
* model.py -> base/model.py
* t() method -> tvalues attribute (t() still exists but raises a warning)
*Main changes and additions*
* Numerous bugfixes.
* Time Series Analysis model (tsa)
- Vector Autoregression Models VAR (tsa.VAR)
- Autogressive Models AR (tsa.AR)
- Autoregressive Moving Average Models ARMA (tsa.ARMA)
optionally uses Cython for Kalman Filtering
use setup.py install with option --with-cython
- Baxter-King band-pass filter (tsa.filters.bkfilter)
- Hodrick-Prescott filter (tsa.filters.hpfilter)
- Christiano-Fitzgerald filter (tsa.filters.cffilter)
* Improved maximum likelihood framework uses all available scipy.optimize solvers
* Refactor of the datasets sub-package.
* Added more datasets for examples.
* Removed RPy dependency for running the test suite.
* Refactored the test suite.
* Refactored codebase/directory structure.
* Support for offset and exposure in GLM.
* Removed data_weights argument to GLM.fit for Binomial models.
* New statistical tests, especially diagnostic and specification tests
* Multiple test correction
* General Method of Moment framework in sandbox
* Improved documentation
* and other additions
0.2.0
-----
*Main changes*
* renames for more consistency
RLM.fitted_values -> RLM.fittedvalues
GLMResults.resid_dev -> GLMResults.resid_deviance
* GLMResults, RegressionResults:
lazy calculations, convert attributes to properties with _cache
* fix tests to run without rpy
* expanded examples in examples directory
* add PyDTA to lib.io -- functions for reading Stata .dta binary files
and converting
them to numpy arrays
* made tools.categorical much more robust
* add_constant now takes a prepend argument
* fix GLS to work with only a one column design
*New*
* add four new datasets
- A dataset from the American National Election Studies (1996)
- Grunfeld (1950) investment data
- Spector and Mazzeo (1980) program effectiveness data
- A US macroeconomic dataset
* add four new Maximum Likelihood Estimators for models with a discrete
dependent variables with examples
- Logit
- Probit
- MNLogit (multinomial logit)
- Poisson
*Sandbox*
* add qqplot in sandbox.graphics
* add sandbox.tsa (time series analysis) and sandbox.regression (anova)
* add principal component analysis in sandbox.tools
* add Seemingly Unrelated Regression (SUR) and Two-Stage Least Squares
for systems of equations in sandbox.sysreg.Sem2SLS
* add restricted least squares (RLS)
0.1.0b1
-------
* initial release

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statsmodels/COPYRIGHTS.txt
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The license of scikits.statsmodels can be found in LICENSE.txt
scikits.statsmodels contains code or derivative code from several other
packages. Some modules also note the author of individual contributions, or
author of code that formed the basis for the derived or translated code.
The copyright statements for the datasets are attached to the individual
datasets, most datasets are in public domain, and we don't claim any copyright
on any of them.
In the following, we collect copyright statements of code from other packages,
all of which are either a version of BSD or MIT licensed:
numpy
scipy
pandas
matplotlib
scikits.learn
numpy (scikits.statsmodels.compatnp contains copy of entire model)
------------------------------------------------------------------
Copyright (c) 2005-2009, NumPy 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:
* 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 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
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
---------------------------------------------------------------------
scipy
-----
Copyright (c) 2001, 2002 Enthought, Inc.
All rights reserved.
Copyright (c) 2003-2009 SciPy 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 the Enthought 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.
---------------------------------------------------------------------------
pandas
------
Copyright (c) 2008-2009 AQR Capital Management, LLC
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 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.
----------------------------------------------------------------------
matplotlib (copied from license.py)
LICENSE AGREEMENT FOR MATPLOTLIB %(version)s
--------------------------------------
1. This LICENSE AGREEMENT is between John D. Hunter ("JDH"), and the
Individual or Organization ("Licensee") accessing and otherwise using
matplotlib software in source or binary form and its associated
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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
matplotlib %(version)s alone or in any derivative version prepared by
Licensee.
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.
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
DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF MATPLOTLIB %(version)s
WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
5. JDH SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB
%(version)s FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR
LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING
MATPLOTLIB %(version)s, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF
THE POSSIBILITY THEREOF.
6. This License Agreement will automatically terminate upon a material
breach of its terms and conditions.
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
trademarks or trade name in a trademark sense to endorse or promote
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8. By copying, installing or otherwise using matplotlib %(version)s,
Licensee agrees to be bound by the terms and conditions of this License
Agreement.
--------------------------------------------------------------------------
scikits.learn
-------------
New BSD License
Copyright (c) 2007 - 2010 Scikit-Learn 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 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.
---------------------------------------------------------------------------

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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/

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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.

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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

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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.

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statsmodels/scikits/__init__.py
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__import__('pkg_resources').declare_namespace(__name__)

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statsmodels/scikits/statsmodels/LICENSE.txt
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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.

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statsmodels/scikits/statsmodels/TODO.txt
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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)

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statsmodels/scikits/statsmodels/__init__.py
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#
# 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

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statsmodels/scikits/statsmodels/api.py
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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

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statsmodels/scikits/statsmodels/base/__init__.py
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statsmodels/scikits/statsmodels/base/data.py
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"""
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)

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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)

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statsmodels/scikits/statsmodels/compatnp/py3k.py
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"""
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)

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statsmodels/scikits/statsmodels/datasets/COPYING
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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.

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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.

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statsmodels/scikits/statsmodels/datasets/__init__.py
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"""
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

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statsmodels/scikits/statsmodels/datasets/anes96/__init__.py
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from data import *

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'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
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714 0 2 6 2 6 6 31 5 21 1
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716 75 2 3 2 5 5 55 7 21 0
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730 93 7 4 3 5 3 36 4 21 1
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820 8 1 5 2 4 5 36 7 22 1
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116
statsmodels/scikits/statsmodels/datasets/anes96/data.py
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"""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

945
statsmodels/scikits/statsmodels/datasets/anes96/src/anes96.csv
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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
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915 9 3 3 2 5 1 47 6 24 0 1
916 0 1 6 1 5 6 52 7 24 1 -4
917 9 0 6 2 6 6 33 6 24 1 -4
918 0 4 4 2 6 6 50 4 24 1 0
919 18 7 6 2 5 4 48 7 24 1 -3
920 19 3 2 2 6 0 36 6 24 0 4
921 31 3 2 3 6 1 35 7 24 0 3
922 3500 7 7 3 5 4 34 7 24 0 -2
923 0 7 2 4 5 2 53 6 24 0 1
924 33 0 4 3 6 2 33 7 24 0 1
925 0 1 6 3 6 6 52 6 24 1 -3
926 18 3 4 3 6 4 44 7 24 0 1
927 0 0 3 4 4 0 48 6 24 0 0
928 31 3 5 2 6 5 20 4 24 1 -2
929 0 5 3 2 4 6 45 6 24 1 0
930 59 7 4 2 6 2 70 3 24 0 0
931 0 0 3 3 4 2 39 3 24 0 1
932 7300 7 3 3 5 1 40 7 24 1 2
933 75 4 5 2 7 5 62 6 24 1 -1
934 0 7 5 2 6 4 46 6 24 1 -2
935 27 7 4 4 7 2 46 3 24 0 3
936 1600 7 4 2 5 6 56 7 24 1 -1
937 0 7 6 3 6 6 55 7 24 1 -3
938 0 7 6 2 6 6 41 4 24 1 -4
939 7300 1 2 3 6 0 43 7 24 0 3
940 16 7 7 1 7 6 34 3 24 1 -6
941 0 7 7 1 6 4 73 6 24 1 -5
942 0 7 5 2 6 6 50 6 24 1 -2
943 0 3 6 2 7 5 43 6 24 1 -3
944 0 6 6 2 5 6 46 7 24 1 -3
945 18 7 4 2 6 3 61 7 24 1 0

13
statsmodels/scikits/statsmodels/datasets/ccard/R_wls.s
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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')

1
statsmodels/scikits/statsmodels/datasets/ccard/__init__.py
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from data import *

73
statsmodels/scikits/statsmodels/datasets/ccard/ccard.csv
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@ -1,73 +0,0 @@
"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
1 AVGEXP AGE INCOME INCOMESQ OWNRENT
2 124.98 38 4.52 20.4304 1
3 9.85 33 2.42 5.8564 0
4 15 34 4.5 20.25 1
5 137.87 31 2.54 6.4516 0
6 546.5 32 9.79 95.8441 1
7 92 23 2.5 6.25 0
8 40.83 28 3.96 15.6816 0
9 150.79 29 2.37 5.6169 1
10 777.82 37 3.8 14.44 1
11 52.58 28 3.2 10.24 0
12 256.66 31 3.95 15.6025 1
13 78.87 29 2.45 6.0025 1
14 42.62 35 1.91 3.6481 1
15 335.43 41 3.2 10.24 1
16 248.72 40 4 16 1
17 548.03 40 10 100 1
18 43.34 35 2.35 5.5225 1
19 218.52 34 2 4 1
20 170.64 36 4 16 0
21 37.58 43 5.14 26.4196 1
22 502.2 30 4.51 20.3401 0
23 73.18 22 1.5 2.25 0
24 1532.77 40 5.5 30.25 1
25 42.69 22 2.03 4.1209 0
26 417.83 29 3.2 10.24 0
27 552.72 21 2.47 6.1009 1
28 222.54 24 3 9 0
29 541.3 43 3.54 12.5316 1
30 568.77 37 5.7 32.49 1
31 344.47 27 3.5 12.25 0
32 405.35 28 4.6 21.16 1
33 310.94 26 3 9 1
34 53.65 23 2.59 6.7081 0
35 63.92 30 1.51 2.2801 0
36 165.85 30 1.85 3.4225 0
37 9.58 38 2.6 6.76 0
38 319.49 36 2 4 0
39 83.08 26 2.35 5.5225 0
40 644.83 28 7 49 1
41 93.2 24 2 4 0
42 105.04 21 1.7 2.89 0
43 34.13 24 2.8 7.84 0
44 41.19 26 2.4 5.76 0
45 169.89 33 3 9 0
46 1898.03 34 4.8 23.04 0
47 810.39 33 3.18 10.1124 0
48 32.78 21 1.5 2.25 0
49 95.8 25 3 9 0
50 27.78 27 2.28 5.1984 0
51 215.07 26 2.8 7.84 0
52 79.51 22 2.7 7.29 0
53 306.03 41 6 36 0
54 104.54 42 3.9 15.21 0
55 642.47 25 3.07 9.4249 0
56 308.05 31 2.46 6.0516 1
57 186.35 27 2 4 0
58 56.15 33 3.25 10.5625 0
59 129.37 37 2.72 7.3984 0
60 93.11 27 2.2 4.84 0
61 292.66 24 3.75 14.0625 0
62 98.46 25 2.88 8.2944 0
63 258.55 36 3.05 9.3025 0
64 101.68 33 2.55 6.5025 0
65 65.25 55 2.64 6.9696 1
66 108.61 20 1.65 2.7225 0
67 49.56 29 2.4 5.76 0
68 235.57 41 7.24 52.4176 1
69 68.38 43 2.4 5.76 0
70 474.15 33 6 36 1
71 234.05 25 3.6 12.96 0
72 451.2 26 5 25 1
73 251.52 46 5.5 30.25 1

57
statsmodels/scikits/statsmodels/datasets/ccard/data.py
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"""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

101
statsmodels/scikits/statsmodels/datasets/ccard/src/ccard.csv
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"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
1 MDR Acc Age Income Avgexp Ownrent Selfempl
2 0 1 38 4.52 124.98 1 0
3 0 1 33 2.42 9.85 0 0
4 0 1 34 4.5 15 1 0
5 0 1 31 2.54 137.87 0 0
6 0 1 32 9.79 546.5 1 0
7 0 1 23 2.5 92 0 0
8 0 1 28 3.96 40.83 0 0
9 0 1 29 2.37 150.79 1 0
10 0 1 37 3.8 777.82 1 0
11 0 1 28 3.2 52.58 0 0
12 0 1 31 3.95 256.66 1 0
13 0 0 42 1.98 0 1 0
14 0 0 30 1.73 0 1 0
15 0 1 29 2.45 78.87 1 0
16 0 1 35 1.91 42.62 1 0
17 0 1 41 3.2 335.43 1 0
18 0 1 40 4 248.72 1 0
19 7 0 30 3 0 1 0
20 0 1 40 10 548.03 1 1
21 3 0 46 3.4 0 0 0
22 0 1 35 2.35 43.34 1 0
23 1 0 25 1.88 0 0 0
24 0 1 34 2 218.52 1 0
25 1 1 36 4 170.64 0 0
26 0 1 43 5.14 37.58 1 0
27 0 1 30 4.51 502.2 0 0
28 0 0 22 3.84 0 0 1
29 0 1 22 1.5 73.18 0 0
30 0 0 34 2.5 0 1 0
31 0 1 40 5.5 1532.77 1 0
32 0 1 22 2.03 42.69 0 0
33 1 1 29 3.2 417.83 0 0
34 1 0 25 3.15 0 1 0
35 0 1 21 2.47 552.72 1 0
36 0 1 24 3 222.54 0 0
37 0 1 43 3.54 541.3 1 0
38 0 0 43 2.28 0 0 0
39 0 1 37 5.7 568.77 1 0
40 0 1 27 3.5 344.47 0 0
41 0 1 28 4.6 405.35 1 0
42 0 1 26 3 310.94 1 0
43 0 1 23 2.59 53.65 0 0
44 0 1 30 1.51 63.92 0 0
45 0 1 30 1.85 165.85 0 0
46 0 1 38 2.6 9.58 0 0
47 0 0 28 1.8 0 0 1
48 0 1 36 2 319.49 0 0
49 0 0 38 3.26 0 0 0
50 0 1 26 2.35 83.08 0 0
51 0 1 28 7 644.83 1 0
52 0 0 50 3.6 0 0 0
53 0 1 24 2 93.2 0 0
54 0 1 21 1.7 105.04 0 0
55 0 1 24 2.8 34.13 0 0
56 0 1 26 2.4 41.19 0 0
57 1 1 33 3 169.89 0 0
58 0 1 34 4.8 1898.03 0 0
59 0 1 33 3.18 810.39 0 0
60 0 0 45 1.8 0 0 0
61 0 1 21 1.5 32.78 0 0
62 2 1 25 3 95.8 0 0
63 0 1 27 2.28 27.78 0 0
64 0 1 26 2.8 215.07 0 0
65 0 1 22 2.7 79.51 0 0
66 3 0 27 4.9 0 1 0
67 0 0 26 2.5 0 0 1
68 0 1 41 6 306.03 0 1
69 0 1 42 3.9 104.54 0 0
70 0 0 22 5.1 0 0 0
71 0 1 25 3.07 642.47 0 0
72 0 1 31 2.46 308.05 1 0
73 0 1 27 2 186.35 0 0
74 0 1 33 3.25 56.15 0 0
75 0 1 37 2.72 129.37 0 0
76 0 1 27 2.2 93.11 0 0
77 1 0 24 4.1 0 0 0
78 0 1 24 3.75 292.66 0 0
79 0 1 25 2.88 98.46 0 0
80 0 1 36 3.05 258.55 0 0
81 0 1 33 2.55 101.68 0 0
82 0 0 33 4 0 0 0
83 1 1 55 2.64 65.25 1 0
84 0 1 20 1.65 108.61 0 0
85 0 1 29 2.4 49.56 0 0
86 3 0 40 3.71 0 0 0
87 0 1 41 7.24 235.57 1 0
88 0 0 41 4.39 0 1 0
89 0 0 35 3.3 0 1 0
90 0 0 24 2.3 0 0 0
91 1 0 54 4.18 0 0 0
92 2 0 34 2.49 0 0 0
93 0 0 45 2.81 0 1 0
94 0 1 43 2.4 68.38 0 0
95 4 0 35 1.5 0 0 0
96 2 0 36 8.4 0 0 0
97 0 1 22 1.56 0 0 0
98 1 1 33 6 474.15 1 0
99 1 1 25 3.6 234.05 0 0
100 0 1 26 5 451.2 1 0
101 0 1 46 5.5 251.52 1 0

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statsmodels/scikits/statsmodels/datasets/ccard/src/names.txt
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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)

11
statsmodels/scikits/statsmodels/datasets/committee/R_committee.s
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### 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)

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statsmodels/scikits/statsmodels/datasets/committee/__init__.py
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from data import *

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statsmodels/scikits/statsmodels/datasets/committee/committee.csv
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"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
1 COMMITTEE BILLS104 SIZE SUBS STAFF PRESTIGE BILLS103
2 Appropriations 6 58 13 109 1 9
3 Budget 23 42 0 39 1 101
4 Rules 44 13 2 25 1 54
5 Ways_and_Means 355 39 5 23 1 542
6 Banking 125 51 5 61 0 101
7 Economic_Educ_Oppor 131 43 5 69 0 158
8 Commerce 271 49 4 79 0 196
9 International_Relations 63 44 3 68 0 40
10 Government_Reform 149 51 7 99 0 72
11 Judiciary 253 35 5 56 0 168
12 Agriculture 81 49 5 46 0 60
13 National_Security 89 55 7 48 0 75
14 Resources 142 44 5 58 0 98
15 TransInfrastructure 155 61 6 74 0 69
16 Science 27 50 4 58 0 25
17 Small_Business 8 43 4 29 0 9
18 Veterans_Affairs 28 33 3 36 0 41
19 House_Oversight 68 12 0 24 0 233
20 Stds_of_Conduct 1 10 0 9 0 0
21 Intelligence 4 16 2 24 0 2

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statsmodels/scikits/statsmodels/datasets/committee/data.py
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"""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

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statsmodels/scikits/statsmodels/datasets/committee/src/committee.dat
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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

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statsmodels/scikits/statsmodels/datasets/copper/__init__.py
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from data import *

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statsmodels/scikits/statsmodels/datasets/copper/copper.csv
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"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
1 YEAR WORLDCONSUMPTION COPPERPRICE INCOMEINDEX ALUMPRICE INVENTORYINDEX TIME
2 1951 3173 26.56 0.7 19.76 0.98 1
3 1952 3281.1 27.31 0.71 20.78 1.04 2
4 1953 3135.7 32.95 0.72 22.55 1.05 3
5 1954 3359.1 33.9 0.7 23.06 0.97 4
6 1955 3755.1 42.7 0.74 24.93 1.02 5
7 1956 3875.9 46.11 0.74 26.5 1.04 6
8 1957 3905.7 31.7 0.74 27.24 0.98 7
9 1958 3957.6 27.23 0.72 26.21 0.98 8
10 1959 4279.1 32.89 0.75 26.09 1.03 9
11 1960 4627.9 33.78 0.77 27.4 1.03 10
12 1961 4910.2 31.66 0.76 26.94 0.98 11
13 1962 4908.4 32.28 0.79 25.18 1 12
14 1963 5327.9 32.38 0.83 23.94 0.97 13
15 1964 5878.4 33.75 0.85 25.07 1.03 14
16 1965 6075.2 36.25 0.89 25.37 1.08 15
17 1966 6312.7 36.24 0.93 24.55 1.05 16
18 1967 6056.8 38.23 0.95 24.98 1.03 17
19 1968 6375.9 40.83 0.99 24.96 1.03 18
20 1969 6974.3 44.62 1 25.52 0.99 19
21 1970 7101.6 52.27 1 26.01 1 20
22 1971 7071.7 45.16 1.02 25.46 0.96 21
23 1972 7754.8 42.5 1.07 22.17 0.97 22
24 1973 8480.3 43.7 1.12 18.56 0.98 23
25 1974 8105.2 47.88 1.1 21.32 1.01 24
26 1975 7157.2 36.33 1.07 22.75 0.94 25

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statsmodels/scikits/statsmodels/datasets/copper/data.py
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"""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)

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statsmodels/scikits/statsmodels/datasets/copper/src/copper.dat
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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

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statsmodels/scikits/statsmodels/datasets/cpunish/R_cpunish.s
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### 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']

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statsmodels/scikits/statsmodels/datasets/cpunish/__init__.py
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from data import *

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statsmodels/scikits/statsmodels/datasets/cpunish/cpunish.csv
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"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
1 STATE EXECUTIONS INCOME PERPOVERTY PERBLACK VC100k96 SOUTH DEGREE
2 Texas 37 34453 16.7 12.2 644 1 0.16
3 Virginia 9 41534 12.5 20 351 1 0.27
4 Missouri 6 35802 10.6 11.2 591 0 0.21
5 Arkansas 4 26954 18.4 16.1 524 1 0.16
6 Alabama 3 31468 14.8 25.9 565 1 0.19
7 Arizona 2 32552 18.8 3.5 632 0 0.25
8 Illinois 2 40873 11.6 15.3 886 0 0.25
9 South_Carolina 2 34861 13.1 30.1 997 1 0.21
10 Colorado 1 42562 9.4 4.3 405 0 0.31
11 Florida 1 31900 14.3 15.4 1051 1 0.24
12 Indiana 1 37421 8.2 8.2 537 0 0.19
13 Kentucky 1 33305 16.4 7.2 321 0 0.16
14 Louisiana 1 32108 18.4 32.1 929 1 0.18
15 Maryland 1 45844 9.3 27.4 931 0 0.29
16 Nebraska 1 34743 10 4 435 0 0.24
17 Oklahoma 1 29709 15.2 7.7 597 0 0.21
18 Oregon 1 36777 11.7 1.8 463 0 0.25

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statsmodels/scikits/statsmodels/datasets/cpunish/data.py
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"""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

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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

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statsmodels/scikits/statsmodels/datasets/grunfeld/__init__.py
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statsmodels/scikits/statsmodels/datasets/grunfeld/data.py
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"""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

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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
1 invest value capital firm year
2 317.6 3078.5 2.8 General Motors 1935
3 391.8 4661.7 52.6 General Motors 1936
4 410.6 5387.1 156.9 General Motors 1937
5 257.7 2792.2 209.2 General Motors 1938
6 330.8 4313.2 203.4 General Motors 1939
7 461.2 4643.9 207.2 General Motors 1940
8 512 4551.2 255.2 General Motors 1941
9 448 3244.1 303.7 General Motors 1942
10 499.6 4053.7 264.1 General Motors 1943
11 547.5 4379.3 201.6 General Motors 1944
12 561.2 4840.9 265 General Motors 1945
13 688.1 4900.9 402.2 General Motors 1946
14 568.9 3526.5 761.5 General Motors 1947
15 529.2 3254.7 922.4 General Motors 1948
16 555.1 3700.2 1020.1 General Motors 1949
17 642.9 3755.6 1099 General Motors 1950
18 755.9 4833 1207.7 General Motors 1951
19 891.2 4924.9 1430.5 General Motors 1952
20 1304.4 6241.7 1777.3 General Motors 1953
21 1486.7 5593.6 2226.3 General Motors 1954
22 209.9 1362.4 53.8 US Steel 1935
23 355.3 1807.1 50.5 US Steel 1936
24 469.9 2676.3 118.1 US Steel 1937
25 262.3 1801.9 260.2 US Steel 1938
26 230.4 1957.3 312.7 US Steel 1939
27 361.6 2202.9 254.2 US Steel 1940
28 472.8 2380.5 261.4 US Steel 1941
29 445.6 2168.6 298.7 US Steel 1942
30 361.6 1985.1 301.8 US Steel 1943
31 288.2 1813.9 279.1 US Steel 1944
32 258.7 1850.2 213.8 US Steel 1945
33 420.3 2067.7 132.6 US Steel 1946
34 420.5 1796.7 264.8 US Steel 1947
35 494.5 1625.8 306.9 US Steel 1948
36 405.1 1667 351.1 US Steel 1949
37 418.8 1677.4 357.8 US Steel 1950
38 588.2 2289.5 342.1 US Steel 1951
39 645.5 2159.4 444.2 US Steel 1952
40 641 2031.3 623.6 US Steel 1953
41 459.3 2115.5 669.7 US Steel 1954
42 33.1 1170.6 97.8 General Electric 1935
43 45 2015.8 104.4 General Electric 1936
44 77.2 2803.3 118 General Electric 1937
45 44.6 2039.7 156.2 General Electric 1938
46 48.1 2256.2 172.6 General Electric 1939
47 74.4 2132.2 186.6 General Electric 1940
48 113 1834.1 220.9 General Electric 1941
49 91.9 1588 287.8 General Electric 1942
50 61.3 1749.4 319.9 General Electric 1943
51 56.8 1687.2 321.3 General Electric 1944
52 93.6 2007.7 319.6 General Electric 1945
53 159.9 2208.3 346 General Electric 1946
54 147.2 1656.7 456.4 General Electric 1947
55 146.3 1604.4 543.4 General Electric 1948
56 98.3 1431.8 618.3 General Electric 1949
57 93.5 1610.5 647.4 General Electric 1950
58 135.2 1819.4 671.3 General Electric 1951
59 157.3 2079.7 726.1 General Electric 1952
60 179.5 2371.6 800.3 General Electric 1953
61 189.6 2759.9 888.9 General Electric 1954
62 40.29 417.5 10.5 Chrysler 1935
63 72.76 837.8 10.2 Chrysler 1936
64 66.26 883.9 34.7 Chrysler 1937
65 51.6 437.9 51.8 Chrysler 1938
66 52.41 679.7 64.3 Chrysler 1939
67 69.41 727.8 67.1 Chrysler 1940
68 68.35 643.6 75.2 Chrysler 1941
69 46.8 410.9 71.4 Chrysler 1942
70 47.4 588.4 67.1 Chrysler 1943
71 59.57 698.4 60.5 Chrysler 1944
72 88.78 846.4 54.6 Chrysler 1945
73 74.12 893.8 84.8 Chrysler 1946
74 62.68 579 96.8 Chrysler 1947
75 89.36 694.6 110.2 Chrysler 1948
76 78.98 590.3 147.4 Chrysler 1949
77 100.66 693.5 163.2 Chrysler 1950
78 160.62 809 203.5 Chrysler 1951
79 145 727 290.6 Chrysler 1952
80 174.93 1001.5 346.1 Chrysler 1953
81 172.49 703.2 414.9 Chrysler 1954
82 39.68 157.7 183.2 Atlantic Refining 1935
83 50.73 167.9 204 Atlantic Refining 1936
84 74.24 192.9 236 Atlantic Refining 1937
85 53.51 156.7 291.7 Atlantic Refining 1938
86 42.65 191.4 323.1 Atlantic Refining 1939
87 46.48 185.5 344 Atlantic Refining 1940
88 61.4 199.6 367.7 Atlantic Refining 1941
89 39.67 189.5 407.2 Atlantic Refining 1942
90 62.24 151.2 426.6 Atlantic Refining 1943
91 52.32 187.7 470 Atlantic Refining 1944
92 63.21 214.7 499.2 Atlantic Refining 1945
93 59.37 232.9 534.6 Atlantic Refining 1946
94 58.02 249 566.6 Atlantic Refining 1947
95 70.34 224.5 595.3 Atlantic Refining 1948
96 67.42 237.3 631.4 Atlantic Refining 1949
97 55.74 240.1 662.3 Atlantic Refining 1950
98 80.3 327.3 683.9 Atlantic Refining 1951
99 85.4 359.4 729.3 Atlantic Refining 1952
100 91.9 398.4 774.3 Atlantic Refining 1953
101 81.43 365.7 804.9 Atlantic Refining 1954
102 20.36 197 6.5 IBM 1935
103 25.98 210.3 15.8 IBM 1936
104 25.94 223.1 27.7 IBM 1937
105 27.53 216.7 39.2 IBM 1938
106 24.6 286.4 48.6 IBM 1939
107 28.54 298 52.5 IBM 1940
108 43.41 276.9 61.5 IBM 1941
109 42.81 272.6 80.5 IBM 1942
110 27.84 287.4 94.4 IBM 1943
111 32.6 330.3 92.6 IBM 1944
112 39.03 324.4 92.3 IBM 1945
113 50.17 401.9 94.2 IBM 1946
114 51.85 407.4 111.4 IBM 1947
115 64.03 409.2 127.4 IBM 1948
116 68.16 482.2 149.3 IBM 1949
117 77.34 673.8 164.4 IBM 1950
118 95.3 676.9 177.2 IBM 1951
119 99.49 702 200 IBM 1952
120 127.52 793.5 211.5 IBM 1953
121 135.72 927.3 238.7 IBM 1954
122 24.43 138 100.2 Union Oil 1935
123 23.21 200.1 125 Union Oil 1936
124 32.78 210.1 142.4 Union Oil 1937
125 32.54 161.2 165.1 Union Oil 1938
126 26.65 161.7 194.8 Union Oil 1939
127 33.71 145.1 222.9 Union Oil 1940
128 43.5 110.6 252.1 Union Oil 1941
129 34.46 98.1 276.3 Union Oil 1942
130 44.28 108.8 300.3 Union Oil 1943
131 70.8 118.2 318.2 Union Oil 1944
132 44.12 126.5 336.2 Union Oil 1945
133 48.98 156.7 351.2 Union Oil 1946
134 48.51 119.4 373.6 Union Oil 1947
135 50 129.1 389.4 Union Oil 1948
136 50.59 134.8 406.7 Union Oil 1949
137 42.53 140.8 429.5 Union Oil 1950
138 64.77 179 450.6 Union Oil 1951
139 72.68 178.1 466.9 Union Oil 1952
140 73.86 186.8 486.2 Union Oil 1953
141 89.51 192.7 511.3 Union Oil 1954
142 12.93 191.5 1.8 Westinghouse 1935
143 25.9 516 0.8 Westinghouse 1936
144 35.05 729 7.4 Westinghouse 1937
145 22.89 560.4 18.1 Westinghouse 1938
146 18.84 519.9 23.5 Westinghouse 1939
147 28.57 628.5 26.5 Westinghouse 1940
148 48.51 537.1 36.2 Westinghouse 1941
149 43.34 561.2 60.8 Westinghouse 1942
150 37.02 617.2 84.4 Westinghouse 1943
151 37.81 626.7 91.2 Westinghouse 1944
152 39.27 737.2 92.4 Westinghouse 1945
153 53.46 760.5 86 Westinghouse 1946
154 55.56 581.4 111.1 Westinghouse 1947
155 49.56 662.3 130.6 Westinghouse 1948
156 32.04 583.8 141.8 Westinghouse 1949
157 32.24 635.2 136.7 Westinghouse 1950
158 54.38 723.8 129.7 Westinghouse 1951
159 71.78 864.1 145.5 Westinghouse 1952
160 90.08 1193.5 174.8 Westinghouse 1953
161 68.6 1188.9 213.5 Westinghouse 1954
162 26.63 290.6 162 Goodyear 1935
163 23.39 291.1 174 Goodyear 1936
164 30.65 335 183 Goodyear 1937
165 20.89 246 198 Goodyear 1938
166 28.78 356.2 208 Goodyear 1939
167 26.93 289.8 223 Goodyear 1940
168 32.08 268.2 234 Goodyear 1941
169 32.21 213.3 248 Goodyear 1942
170 35.69 348.2 274 Goodyear 1943
171 62.47 374.2 282 Goodyear 1944
172 52.32 387.2 316 Goodyear 1945
173 56.95 347.4 302 Goodyear 1946
174 54.32 291.9 333 Goodyear 1947
175 40.53 297.2 359 Goodyear 1948
176 32.54 276.9 370 Goodyear 1949
177 43.48 274.6 376 Goodyear 1950
178 56.49 339.9 391 Goodyear 1951
179 65.98 474.8 414 Goodyear 1952
180 66.11 496 443 Goodyear 1953
181 49.34 474.5 468 Goodyear 1954
182 2.54 70.91 4.5 Diamond Match 1935
183 2 87.94 4.71 Diamond Match 1936
184 2.19 82.2 4.57 Diamond Match 1937
185 1.99 58.72 4.56 Diamond Match 1938
186 2.03 80.54 4.38 Diamond Match 1939
187 1.81 86.47 4.21 Diamond Match 1940
188 2.14 77.68 4.12 Diamond Match 1941
189 1.86 62.16 3.83 Diamond Match 1942
190 0.93 62.24 3.58 Diamond Match 1943
191 1.18 61.82 3.41 Diamond Match 1944
192 1.36 65.85 3.31 Diamond Match 1945
193 2.24 69.54 3.23 Diamond Match 1946
194 3.81 64.97 3.9 Diamond Match 1947
195 5.66 68 5.38 Diamond Match 1948
196 4.21 71.24 7.39 Diamond Match 1949
197 3.42 69.05 8.74 Diamond Match 1950
198 4.67 83.04 9.07 Diamond Match 1951
199 6 74.42 9.93 Diamond Match 1952
200 6.53 63.51 11.68 Diamond Match 1953
201 5.12 58.12 14.33 Diamond Match 1954
202 2.938 30.284 52.011 American Steel 1935
203 5.643 43.909 52.903 American Steel 1936
204 10.233 107.02 54.499 American Steel 1937
205 4.046 68.306 59.722 American Steel 1938
206 3.326 84.164 61.659 American Steel 1939
207 4.68 69.157 62.243 American Steel 1940
208 5.732 60.148 63.361 American Steel 1941
209 12.117 49.332 64.861 American Steel 1942
210 15.276 75.18 67.953 American Steel 1943
211 9.275 62.05 69.59 American Steel 1944
212 9.577 59.152 69.144 American Steel 1945
213 3.956 68.424 70.269 American Steel 1946
214 3.834 48.505 71.051 American Steel 1947
215 5.97 40.507 71.508 American Steel 1948
216 6.433 39.961 73.827 American Steel 1949
217 4.77 36.494 75.847 American Steel 1950
218 6.532 46.082 77.367 American Steel 1951
219 7.329 57.616 78.631 American Steel 1952
220 9.02 57.441 80.215 American Steel 1953
221 6.281 47.165 83.788 American Steel 1954

221
statsmodels/scikits/statsmodels/datasets/grunfeld/src/grunfeld.csv
Unescape Escape View File

@ -1,221 +0,0 @@
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
1 invest value capital firm year
2 317.6 3078.5 2.8 General Motors 1935
3 391.8 4661.7 52.6 General Motors 1936
4 410.6 5387.1 156.9 General Motors 1937
5 257.7 2792.2 209.2 General Motors 1938
6 330.8 4313.2 203.4 General Motors 1939
7 461.2 4643.9 207.2 General Motors 1940
8 512 4551.2 255.2 General Motors 1941
9 448 3244.1 303.7 General Motors 1942
10 499.6 4053.7 264.1 General Motors 1943
11 547.5 4379.3 201.6 General Motors 1944
12 561.2 4840.9 265 General Motors 1945
13 688.1 4900.9 402.2 General Motors 1946
14 568.9 3526.5 761.5 General Motors 1947
15 529.2 3254.7 922.4 General Motors 1948
16 555.1 3700.2 1020.1 General Motors 1949
17 642.9 3755.6 1099 General Motors 1950
18 755.9 4833 1207.7 General Motors 1951
19 891.2 4924.9 1430.5 General Motors 1952
20 1304.4 6241.7 1777.3 General Motors 1953
21 1486.7 5593.6 2226.3 General Motors 1954
22 209.9 1362.4 53.8 US Steel 1935
23 355.3 1807.1 50.5 US Steel 1936
24 469.9 2676.3 118.1 US Steel 1937
25 262.3 1801.9 260.2 US Steel 1938
26 230.4 1957.3 312.7 US Steel 1939
27 361.6 2202.9 254.2 US Steel 1940
28 472.8 2380.5 261.4 US Steel 1941
29 445.6 2168.6 298.7 US Steel 1942
30 361.6 1985.1 301.8 US Steel 1943
31 288.2 1813.9 279.1 US Steel 1944
32 258.7 1850.2 213.8 US Steel 1945
33 420.3 2067.7 132.6 US Steel 1946
34 420.5 1796.7 264.8 US Steel 1947
35 494.5 1625.8 306.9 US Steel 1948
36 405.1 1667 351.1 US Steel 1949
37 418.8 1677.4 357.8 US Steel 1950
38 588.2 2289.5 342.1 US Steel 1951
39 645.5 2159.4 444.2 US Steel 1952
40 641 2031.3 623.6 US Steel 1953
41 459.3 2115.5 669.7 US Steel 1954
42 33.1 1170.6 97.8 General Electric 1935
43 45 2015.8 104.4 General Electric 1936
44 77.2 2803.3 118 General Electric 1937
45 44.6 2039.7 156.2 General Electric 1938
46 48.1 2256.2 172.6 General Electric 1939
47 74.4 2132.2 186.6 General Electric 1940
48 113 1834.1 220.9 General Electric 1941
49 91.9 1588 287.8 General Electric 1942
50 61.3 1749.4 319.9 General Electric 1943
51 56.8 1687.2 321.3 General Electric 1944
52 93.6 2007.7 319.6 General Electric 1945
53 159.9 2208.3 346 General Electric 1946
54 147.2 1656.7 456.4 General Electric 1947
55 146.3 1604.4 543.4 General Electric 1948
56 98.3 1431.8 618.3 General Electric 1949
57 93.5 1610.5 647.4 General Electric 1950
58 135.2 1819.4 671.3 General Electric 1951
59 157.3 2079.7 726.1 General Electric 1952
60 179.5 2371.6 800.3 General Electric 1953
61 189.6 2759.9 888.9 General Electric 1954
62 40.29 417.5 10.5 Chrysler 1935
63 72.76 837.8 10.2 Chrysler 1936
64 66.26 883.9 34.7 Chrysler 1937
65 51.6 437.9 51.8 Chrysler 1938
66 52.41 679.7 64.3 Chrysler 1939
67 69.41 727.8 67.1 Chrysler 1940
68 68.35 643.6 75.2 Chrysler 1941
69 46.8 410.9 71.4 Chrysler 1942
70 47.4 588.4 67.1 Chrysler 1943
71 59.57 698.4 60.5 Chrysler 1944
72 88.78 846.4 54.6 Chrysler 1945
73 74.12 893.8 84.8 Chrysler 1946
74 62.68 579 96.8 Chrysler 1947
75 89.36 694.6 110.2 Chrysler 1948
76 78.98 590.3 147.4 Chrysler 1949
77 100.66 693.5 163.2 Chrysler 1950
78 160.62 809 203.5 Chrysler 1951
79 145 727 290.6 Chrysler 1952
80 174.93 1001.5 346.1 Chrysler 1953
81 172.49 703.2 414.9 Chrysler 1954
82 39.68 157.7 183.2 Atlantic Refining 1935
83 50.73 167.9 204 Atlantic Refining 1936
84 74.24 192.9 236 Atlantic Refining 1937
85 53.51 156.7 291.7 Atlantic Refining 1938
86 42.65 191.4 323.1 Atlantic Refining 1939
87 46.48 185.5 344 Atlantic Refining 1940
88 61.4 199.6 367.7 Atlantic Refining 1941
89 39.67 189.5 407.2 Atlantic Refining 1942
90 62.24 151.2 426.6 Atlantic Refining 1943
91 52.32 187.7 470 Atlantic Refining 1944
92 63.21 214.7 499.2 Atlantic Refining 1945
93 59.37 232.9 534.6 Atlantic Refining 1946
94 58.02 249 566.6 Atlantic Refining 1947
95 70.34 224.5 595.3 Atlantic Refining 1948
96 67.42 237.3 631.4 Atlantic Refining 1949
97 55.74 240.1 662.3 Atlantic Refining 1950
98 80.3 327.3 683.9 Atlantic Refining 1951
99 85.4 359.4 729.3 Atlantic Refining 1952
100 91.9 398.4 774.3 Atlantic Refining 1953
101 81.43 365.7 804.9 Atlantic Refining 1954
102 20.36 197 6.5 IBM 1935
103 25.98 210.3 15.8 IBM 1936
104 25.94 223.1 27.7 IBM 1937
105 27.53 216.7 39.2 IBM 1938
106 24.6 286.4 48.6 IBM 1939
107 28.54 298 52.5 IBM 1940
108 43.41 276.9 61.5 IBM 1941
109 42.81 272.6 80.5 IBM 1942
110 27.84 287.4 94.4 IBM 1943
111 32.6 330.3 92.6 IBM 1944
112 39.03 324.4 92.3 IBM 1945
113 50.17 401.9 94.2 IBM 1946
114 51.85 407.4 111.4 IBM 1947
115 64.03 409.2 127.4 IBM 1948
116 68.16 482.2 149.3 IBM 1949
117 77.34 673.8 164.4 IBM 1950
118 95.3 676.9 177.2 IBM 1951
119 99.49 702 200 IBM 1952
120 127.52 793.5 211.5 IBM 1953
121 135.72 927.3 238.7 IBM 1954
122 24.43 138 100.2 Union Oil 1935
123 23.21 200.1 125 Union Oil 1936
124 32.78 210.1 142.4 Union Oil 1937
125 32.54 161.2 165.1 Union Oil 1938
126 26.65 161.7 194.8 Union Oil 1939
127 33.71 145.1 222.9 Union Oil 1940
128 43.5 110.6 252.1 Union Oil 1941
129 34.46 98.1 276.3 Union Oil 1942
130 44.28 108.8 300.3 Union Oil 1943
131 70.8 118.2 318.2 Union Oil 1944
132 44.12 126.5 336.2 Union Oil 1945
133 48.98 156.7 351.2 Union Oil 1946
134 48.51 119.4 373.6 Union Oil 1947
135 50 129.1 389.4 Union Oil 1948
136 50.59 134.8 406.7 Union Oil 1949
137 42.53 140.8 429.5 Union Oil 1950
138 64.77 179 450.6 Union Oil 1951
139 72.68 178.1 466.9 Union Oil 1952
140 73.86 186.8 486.2 Union Oil 1953
141 89.51 192.7 511.3 Union Oil 1954
142 12.93 191.5 1.8 Westinghouse 1935
143 25.9 516 0.8 Westinghouse 1936
144 35.05 729 7.4 Westinghouse 1937
145 22.89 560.4 18.1 Westinghouse 1938
146 18.84 519.9 23.5 Westinghouse 1939
147 28.57 628.5 26.5 Westinghouse 1940
148 48.51 537.1 36.2 Westinghouse 1941
149 43.34 561.2 60.8 Westinghouse 1942
150 37.02 617.2 84.4 Westinghouse 1943
151 37.81 626.7 91.2 Westinghouse 1944
152 39.27 737.2 92.4 Westinghouse 1945
153 53.46 760.5 86 Westinghouse 1946
154 55.56 581.4 111.1 Westinghouse 1947
155 49.56 662.3 130.6 Westinghouse 1948
156 32.04 583.8 141.8 Westinghouse 1949
157 32.24 635.2 136.7 Westinghouse 1950
158 54.38 723.8 129.7 Westinghouse 1951
159 71.78 864.1 145.5 Westinghouse 1952
160 90.08 1193.5 174.8 Westinghouse 1953
161 68.6 1188.9 213.5 Westinghouse 1954
162 26.63 290.6 162 Goodyear 1935
163 23.39 291.1 174 Goodyear 1936
164 30.65 335 183 Goodyear 1937
165 20.89 246 198 Goodyear 1938
166 28.78 356.2 208 Goodyear 1939
167 26.93 289.8 223 Goodyear 1940
168 32.08 268.2 234 Goodyear 1941
169 32.21 213.3 248 Goodyear 1942
170 35.69 348.2 274 Goodyear 1943
171 62.47 374.2 282 Goodyear 1944
172 52.32 387.2 316 Goodyear 1945
173 56.95 347.4 302 Goodyear 1946
174 54.32 291.9 333 Goodyear 1947
175 40.53 297.2 359 Goodyear 1948
176 32.54 276.9 370 Goodyear 1949
177 43.48 274.6 376 Goodyear 1950
178 56.49 339.9 391 Goodyear 1951
179 65.98 474.8 414 Goodyear 1952
180 66.11 496 443 Goodyear 1953
181 49.34 474.5 468 Goodyear 1954
182 2.54 70.91 4.5 Diamond Match 1935
183 2 87.94 4.71 Diamond Match 1936
184 2.19 82.2 4.57 Diamond Match 1937
185 1.99 58.72 4.56 Diamond Match 1938
186 2.03 80.54 4.38 Diamond Match 1939
187 1.81 86.47 4.21 Diamond Match 1940
188 2.14 77.68 4.12 Diamond Match 1941
189 1.86 62.16 3.83 Diamond Match 1942
190 0.93 62.24 3.58 Diamond Match 1943
191 1.18 61.82 3.41 Diamond Match 1944
192 1.36 65.85 3.31 Diamond Match 1945
193 2.24 69.54 3.23 Diamond Match 1946
194 3.81 64.97 3.9 Diamond Match 1947
195 5.66 68 5.38 Diamond Match 1948
196 4.21 71.24 7.39 Diamond Match 1949
197 3.42 69.05 8.74 Diamond Match 1950
198 4.67 83.04 9.07 Diamond Match 1951
199 6 74.42 9.93 Diamond Match 1952
200 6.53 63.51 11.68 Diamond Match 1953
201 5.12 58.12 14.33 Diamond Match 1954
202 2.938 30.284 52.011 American Steel 1935
203 5.643 43.909 52.903 American Steel 1936
204 10.233 107.02 54.499 American Steel 1937
205 4.046 68.306 59.722 American Steel 1938
206 3.326 84.164 61.659 American Steel 1939
207 4.68 69.157 62.243 American Steel 1940
208 5.732 60.148 63.361 American Steel 1941
209 12.117 49.332 64.861 American Steel 1942
210 15.276 75.18 67.953 American Steel 1943
211 9.275 62.05 69.59 American Steel 1944
212 9.577 59.152 69.144 American Steel 1945
213 3.956 68.424 70.269 American Steel 1946
214 3.834 48.505 71.051 American Steel 1947
215 5.97 40.507 71.508 American Steel 1948
216 6.433 39.961 73.827 American Steel 1949
217 4.77 36.494 75.847 American Steel 1950
218 6.532 46.082 77.367 American Steel 1951
219 7.329 57.616 78.631 American Steel 1952
220 9.02 57.441 80.215 American Steel 1953
221 6.281 47.165 83.788 American Steel 1954

37
statsmodels/scikits/statsmodels/datasets/longley/R_gls.s
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### 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)))

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statsmodels/scikits/statsmodels/datasets/longley/R_lm.s
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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)

1
statsmodels/scikits/statsmodels/datasets/longley/__init__.py
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from data import *

73
statsmodels/scikits/statsmodels/datasets/longley/data.py
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"""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

17
statsmodels/scikits/statsmodels/datasets/longley/longley.csv
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"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
1 Obs TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 1 60323 83 234289 2356 1590 107608 1947
3 2 61122 88.5 259426 2325 1456 108632 1948
4 3 60171 88.2 258054 3682 1616 109773 1949
5 4 61187 89.5 284599 3351 1650 110929 1950
6 5 63221 96.2 328975 2099 3099 112075 1951
7 6 63639 98.1 346999 1932 3594 113270 1952
8 7 64989 99 365385 1870 3547 115094 1953
9 8 63761 100 363112 3578 3350 116219 1954
10 9 66019 101.2 397469 2904 3048 117388 1955
11 10 67857 104.6 419180 2822 2857 118734 1956
12 11 68169 108.4 442769 2936 2798 120445 1957
13 12 66513 110.8 444546 4681 2637 121950 1958
14 13 68655 112.6 482704 3813 2552 123366 1959
15 14 69564 114.2 502601 3931 2514 125368 1960
16 15 69331 115.7 518173 4806 2572 127852 1961
17 16 70551 116.9 554894 4007 2827 130081 1962

1
statsmodels/scikits/statsmodels/datasets/macrodata/__init__.py
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from data import *

89
statsmodels/scikits/statsmodels/datasets/macrodata/data.py
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"""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

204
statsmodels/scikits/statsmodels/datasets/macrodata/macrodata.csv
Unescape Escape View File

@ -1,204 +0,0 @@
"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
1 year quarter realgdp realcons realinv realgovt realdpi cpi m1 tbilrate unemp pop infl realint
2 1959 1 2710.349 1707.4 286.898 470.045 1886.9 28.980 139.7 2.82 5.8 177.146 0 0
3 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
4 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
5 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
6 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
7 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
8 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
9 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
10 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
11 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
12 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
13 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
14 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
15 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
16 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
17 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
18 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
19 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
20 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
21 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
22 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
23 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
24 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
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
26 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
27 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
28 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
29 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
30 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
31 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
32 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
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
34 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
35 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
36 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
37 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
38 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
39 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
40 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
41 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
42 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
43 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
44 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
45 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
46 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
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
48 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
49 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
50 1971 1 4374.016 2802.2 517.935 542.709 3197.3 40.100 220 3.65 5.9 207.065 2 1.65
51 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
52 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
53 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
54 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
55 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
56 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
57 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
58 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
59 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
60 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
61 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
62 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
63 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
64 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
65 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
66 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
67 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
68 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
69 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
70 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
71 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
72 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
73 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
74 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
75 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
76 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
77 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
78 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
79 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
80 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
81 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
82 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
83 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
84 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
85 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
86 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
87 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
88 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
89 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
90 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
91 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
92 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
93 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
94 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
95 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
96 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
97 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
98 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
99 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
100 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
101 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
102 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
103 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
104 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
105 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
106 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
107 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
108 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
109 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
110 1986 1 7022.757 4639.3 967.442 728.125 5255.8 108.700 641 6.56 7 240.094 -4.39 10.95
111 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
112 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
113 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
114 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
115 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
116 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
117 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
118 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
119 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
120 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
121 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
122 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
123 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
124 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
125 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
126 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
127 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
128 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
129 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
130 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
131 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
132 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
133 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
134 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
135 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
136 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
137 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
138 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
139 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
140 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
141 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
142 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
143 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
144 1994 3 8904.289 5938.7 1212.655 736.890 6487.9 149.400 1150.1 4.68 6 264.301 2.69 2
145 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
146 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
147 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
148 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
149 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
150 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
151 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
152 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
153 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
154 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
155 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
156 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
157 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
158 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
159 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
160 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
161 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
162 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
163 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
164 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
165 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
166 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
167 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
168 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
169 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
170 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
171 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
172 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
173 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
174 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
175 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
176 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
177 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
178 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
179 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
180 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
181 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
182 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
183 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
184 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
185 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
186 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
187 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
188 2005 3 12683.153 8865.6 2154.949 890.394 9308.0 199.200 1375 3.52 5 296.770 9.14 -5.62
189 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
190 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
191 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
192 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
193 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
194 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
195 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
196 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
197 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
198 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
199 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
200 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
201 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
202 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
203 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
204 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

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statsmodels/scikits/statsmodels/datasets/macrodata/src/unemp.csv
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@ -1,214 +0,0 @@
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
1 Series Id: LNS14000000Q
2 Seasonally Adjusted
3 Series title: (Seas) Unemployment Rate
4 Labor force status: Unemployment rate
5 Type of data: Percent or rate
6 Age: 16 years and over
7 Series id,Year,Period,Value,
8 LNS14000000Q,1959,Q01,5.8
9 LNS14000000Q,1959,Q02,5.1
10 LNS14000000Q,1959,Q03,5.3
11 LNS14000000Q,1959,Q04,5.6
12 LNS14000000Q,1960,Q01,5.2
13 LNS14000000Q,1960,Q02,5.2
14 LNS14000000Q,1960,Q03,5.6
15 LNS14000000Q,1960,Q04,6.3
16 LNS14000000Q,1961,Q01,6.8
17 LNS14000000Q,1961,Q02,7.0
18 LNS14000000Q,1961,Q03,6.8
19 LNS14000000Q,1961,Q04,6.2
20 LNS14000000Q,1962,Q01,5.6
21 LNS14000000Q,1962,Q02,5.5
22 LNS14000000Q,1962,Q03,5.6
23 LNS14000000Q,1962,Q04,5.5
24 LNS14000000Q,1963,Q01,5.8
25 LNS14000000Q,1963,Q02,5.7
26 LNS14000000Q,1963,Q03,5.5
27 LNS14000000Q,1963,Q04,5.6
28 LNS14000000Q,1964,Q01,5.5
29 LNS14000000Q,1964,Q02,5.2
30 LNS14000000Q,1964,Q03,5.0
31 LNS14000000Q,1964,Q04,5.0
32 LNS14000000Q,1965,Q01,4.9
33 LNS14000000Q,1965,Q02,4.7
34 LNS14000000Q,1965,Q03,4.4
35 LNS14000000Q,1965,Q04,4.1
36 LNS14000000Q,1966,Q01,3.9
37 LNS14000000Q,1966,Q02,3.8
38 LNS14000000Q,1966,Q03,3.8
39 LNS14000000Q,1966,Q04,3.7
40 LNS14000000Q,1967,Q01,3.8
41 LNS14000000Q,1967,Q02,3.8
42 LNS14000000Q,1967,Q03,3.8
43 LNS14000000Q,1967,Q04,3.9
44 LNS14000000Q,1968,Q01,3.7
45 LNS14000000Q,1968,Q02,3.5
46 LNS14000000Q,1968,Q03,3.5
47 LNS14000000Q,1968,Q04,3.4
48 LNS14000000Q,1969,Q01,3.4
49 LNS14000000Q,1969,Q02,3.4
50 LNS14000000Q,1969,Q03,3.6
51 LNS14000000Q,1969,Q04,3.6
52 LNS14000000Q,1970,Q01,4.2
53 LNS14000000Q,1970,Q02,4.8
54 LNS14000000Q,1970,Q03,5.2
55 LNS14000000Q,1970,Q04,5.8
56 LNS14000000Q,1971,Q01,5.9
57 LNS14000000Q,1971,Q02,5.9
58 LNS14000000Q,1971,Q03,6.0
59 LNS14000000Q,1971,Q04,6.0
60 LNS14000000Q,1972,Q01,5.8
61 LNS14000000Q,1972,Q02,5.7
62 LNS14000000Q,1972,Q03,5.6
63 LNS14000000Q,1972,Q04,5.3
64 LNS14000000Q,1973,Q01,5.0
65 LNS14000000Q,1973,Q02,4.9
66 LNS14000000Q,1973,Q03,4.8
67 LNS14000000Q,1973,Q04,4.8
68 LNS14000000Q,1974,Q01,5.1
69 LNS14000000Q,1974,Q02,5.2
70 LNS14000000Q,1974,Q03,5.6
71 LNS14000000Q,1974,Q04,6.6
72 LNS14000000Q,1975,Q01,8.2
73 LNS14000000Q,1975,Q02,8.9
74 LNS14000000Q,1975,Q03,8.5
75 LNS14000000Q,1975,Q04,8.3
76 LNS14000000Q,1976,Q01,7.7
77 LNS14000000Q,1976,Q02,7.6
78 LNS14000000Q,1976,Q03,7.7
79 LNS14000000Q,1976,Q04,7.8
80 LNS14000000Q,1977,Q01,7.5
81 LNS14000000Q,1977,Q02,7.1
82 LNS14000000Q,1977,Q03,6.9
83 LNS14000000Q,1977,Q04,6.6
84 LNS14000000Q,1978,Q01,6.3
85 LNS14000000Q,1978,Q02,6.0
86 LNS14000000Q,1978,Q03,6.0
87 LNS14000000Q,1978,Q04,5.9
88 LNS14000000Q,1979,Q01,5.9
89 LNS14000000Q,1979,Q02,5.7
90 LNS14000000Q,1979,Q03,5.9
91 LNS14000000Q,1979,Q04,5.9
92 LNS14000000Q,1980,Q01,6.3
93 LNS14000000Q,1980,Q02,7.3
94 LNS14000000Q,1980,Q03,7.7
95 LNS14000000Q,1980,Q04,7.4
96 LNS14000000Q,1981,Q01,7.4
97 LNS14000000Q,1981,Q02,7.4
98 LNS14000000Q,1981,Q03,7.4
99 LNS14000000Q,1981,Q04,8.2
100 LNS14000000Q,1982,Q01,8.8
101 LNS14000000Q,1982,Q02,9.4
102 LNS14000000Q,1982,Q03,9.9
103 LNS14000000Q,1982,Q04,10.7
104 LNS14000000Q,1983,Q01,10.4
105 LNS14000000Q,1983,Q02,10.1
106 LNS14000000Q,1983,Q03,9.4
107 LNS14000000Q,1983,Q04,8.5
108 LNS14000000Q,1984,Q01,7.9
109 LNS14000000Q,1984,Q02,7.5
110 LNS14000000Q,1984,Q03,7.4
111 LNS14000000Q,1984,Q04,7.3
112 LNS14000000Q,1985,Q01,7.3
113 LNS14000000Q,1985,Q02,7.3
114 LNS14000000Q,1985,Q03,7.2
115 LNS14000000Q,1985,Q04,7.0
116 LNS14000000Q,1986,Q01,7.0
117 LNS14000000Q,1986,Q02,7.2
118 LNS14000000Q,1986,Q03,7.0
119 LNS14000000Q,1986,Q04,6.8
120 LNS14000000Q,1987,Q01,6.6
121 LNS14000000Q,1987,Q02,6.3
122 LNS14000000Q,1987,Q03,6.0
123 LNS14000000Q,1987,Q04,5.9
124 LNS14000000Q,1988,Q01,5.7
125 LNS14000000Q,1988,Q02,5.5
126 LNS14000000Q,1988,Q03,5.5
127 LNS14000000Q,1988,Q04,5.3
128 LNS14000000Q,1989,Q01,5.2
129 LNS14000000Q,1989,Q02,5.2
130 LNS14000000Q,1989,Q03,5.3
131 LNS14000000Q,1989,Q04,5.4
132 LNS14000000Q,1990,Q01,5.3
133 LNS14000000Q,1990,Q02,5.3
134 LNS14000000Q,1990,Q03,5.7
135 LNS14000000Q,1990,Q04,6.1
136 LNS14000000Q,1991,Q01,6.6
137 LNS14000000Q,1991,Q02,6.8
138 LNS14000000Q,1991,Q03,6.9
139 LNS14000000Q,1991,Q04,7.1
140 LNS14000000Q,1992,Q01,7.4
141 LNS14000000Q,1992,Q02,7.6
142 LNS14000000Q,1992,Q03,7.6
143 LNS14000000Q,1992,Q04,7.4
144 LNS14000000Q,1993,Q01,7.2
145 LNS14000000Q,1993,Q02,7.1
146 LNS14000000Q,1993,Q03,6.8
147 LNS14000000Q,1993,Q04,6.6
148 LNS14000000Q,1994,Q01,6.6
149 LNS14000000Q,1994,Q02,6.2
150 LNS14000000Q,1994,Q03,6.0
151 LNS14000000Q,1994,Q04,5.6
152 LNS14000000Q,1995,Q01,5.5
153 LNS14000000Q,1995,Q02,5.7
154 LNS14000000Q,1995,Q03,5.7
155 LNS14000000Q,1995,Q04,5.6
156 LNS14000000Q,1996,Q01,5.5
157 LNS14000000Q,1996,Q02,5.5
158 LNS14000000Q,1996,Q03,5.3
159 LNS14000000Q,1996,Q04,5.3
160 LNS14000000Q,1997,Q01,5.2
161 LNS14000000Q,1997,Q02,5.0
162 LNS14000000Q,1997,Q03,4.9
163 LNS14000000Q,1997,Q04,4.7
164 LNS14000000Q,1998,Q01,4.6
165 LNS14000000Q,1998,Q02,4.4
166 LNS14000000Q,1998,Q03,4.5
167 LNS14000000Q,1998,Q04,4.4
168 LNS14000000Q,1999,Q01,4.3
169 LNS14000000Q,1999,Q02,4.3
170 LNS14000000Q,1999,Q03,4.2
171 LNS14000000Q,1999,Q04,4.1
172 LNS14000000Q,2000,Q01,4.0
173 LNS14000000Q,2000,Q02,3.9
174 LNS14000000Q,2000,Q03,4.0
175 LNS14000000Q,2000,Q04,3.9
176 LNS14000000Q,2001,Q01,4.2
177 LNS14000000Q,2001,Q02,4.4
178 LNS14000000Q,2001,Q03,4.8
179 LNS14000000Q,2001,Q04,5.5
180 LNS14000000Q,2002,Q01,5.7
181 LNS14000000Q,2002,Q02,5.8
182 LNS14000000Q,2002,Q03,5.7
183 LNS14000000Q,2002,Q04,5.8
184 LNS14000000Q,2003,Q01,5.9
185 LNS14000000Q,2003,Q02,6.2
186 LNS14000000Q,2003,Q03,6.1
187 LNS14000000Q,2003,Q04,5.8
188 LNS14000000Q,2004,Q01,5.7
189 LNS14000000Q,2004,Q02,5.6
190 LNS14000000Q,2004,Q03,5.4
191 LNS14000000Q,2004,Q04,5.4
192 LNS14000000Q,2005,Q01,5.3
193 LNS14000000Q,2005,Q02,5.1
194 LNS14000000Q,2005,Q03,5.0
195 LNS14000000Q,2005,Q04,4.9
196 LNS14000000Q,2006,Q01,4.7
197 LNS14000000Q,2006,Q02,4.7
198 LNS14000000Q,2006,Q03,4.7
199 LNS14000000Q,2006,Q04,4.4
200 LNS14000000Q,2007,Q01,4.5
201 LNS14000000Q,2007,Q02,4.5
202 LNS14000000Q,2007,Q03,4.7
203 LNS14000000Q,2007,Q04,4.8
204 LNS14000000Q,2008,Q01,4.9
205 LNS14000000Q,2008,Q02,5.4
206 LNS14000000Q,2008,Q03,6.0
207 LNS14000000Q,2008,Q04,6.9
208 LNS14000000Q,2009,Q01,8.1
209 LNS14000000Q,2009,Q02,9.2
210 LNS14000000Q,2009,Q03,9.6

1
statsmodels/scikits/statsmodels/datasets/nile/__init__.py
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from data import *

61
statsmodels/scikits/statsmodels/datasets/nile/data.py
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"""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

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statsmodels/scikits/statsmodels/datasets/nile/nile.csv
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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
1 year volume
2 1871 1120
3 1872 1160
4 1873 963
5 1874 1210
6 1875 1160
7 1876 1160
8 1877 813
9 1878 1230
10 1879 1370
11 1880 1140
12 1881 995
13 1882 935
14 1883 1110
15 1884 994
16 1885 1020
17 1886 960
18 1887 1180
19 1888 799
20 1889 958
21 1890 1140
22 1891 1100
23 1892 1210
24 1893 1150
25 1894 1250
26 1895 1260
27 1896 1220
28 1897 1030
29 1898 1100
30 1899 774
31 1900 840
32 1901 874
33 1902 694
34 1903 940
35 1904 833
36 1905 701
37 1906 916
38 1907 692
39 1908 1020
40 1909 1050
41 1910 969
42 1911 831
43 1912 726
44 1913 456
45 1914 824
46 1915 702
47 1916 1120
48 1917 1100
49 1918 832
50 1919 764
51 1920 821
52 1921 768
53 1922 845
54 1923 864
55 1924 862
56 1925 698
57 1926 845
58 1927 744
59 1928 796
60 1929 1040
61 1930 759
62 1931 781
63 1932 865
64 1933 845
65 1934 944
66 1935 984
67 1936 897
68 1937 822
69 1938 1010
70 1939 771
71 1940 676
72 1941 649
73 1942 846
74 1943 812
75 1944 742
76 1945 801
77 1946 1040
78 1947 860
79 1948 874
80 1949 848
81 1950 890
82 1951 744
83 1952 749
84 1953 838
85 1954 1050
86 1955 918
87 1956 986
88 1957 797
89 1958 923
90 1959 975
91 1960 815
92 1961 1020
93 1962 906
94 1963 901
95 1964 1170
96 1965 912
97 1966 746
98 1967 919
99 1968 718
100 1969 714
101 1970 740

1
statsmodels/scikits/statsmodels/datasets/randhie/__init__.py
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from data import *

87
statsmodels/scikits/statsmodels/datasets/randhie/data.py
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"""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

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statsmodels/scikits/statsmodels/datasets/randhie/randhie.csv
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statsmodels/scikits/statsmodels/datasets/randhie/src/randdesc.txt
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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

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statsmodels/scikits/statsmodels/datasets/scotland/R_scotvote.s
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### 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")

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statsmodels/scikits/statsmodels/datasets/scotland/__init__.py
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from data import *

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statsmodels/scikits/statsmodels/datasets/scotland/data.py
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"""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

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"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
1 COUNCILDIST YES COUTAX UNEMPF MOR ACT GDP AGE COUTAX_FEMALEUNEMP
2 Aberdeen_City 60.3 712 21 105 82.4 13566 12.3 14952
3 Aberdeenshire 52.3 643 26.5 97 80.2 13566 15.3 17039.5
4 Angus 53.4 679 28.3 113 86.3 9611 13.9 19215.7
5 Argyll_and_Bute 57 801 27.1 109 80.4 9483 13.6 21707.1
6 Clackmannanshire 68.7 753 22 115 64.7 9265 14.6 16566
7 Dumfries_and_Galloway 48.8 714 24.3 107 79 9555 13.8 17350.2
8 Dundee_City 65.5 920 21.2 118 72.2 9611 13.3 19504
9 East_Ayrshire 70.5 779 20.5 114 75.2 9483 14.5 15969.5
10 East_Dunbartonshire 59.1 771 23.2 102 81.1 9483 14.2 17887.2
11 East_Lothian 62.7 724 20.5 112 80.3 12656 13.7 14842
12 East_Renfrewshire 51.6 682 23.8 96 83 9483 14.6 16231.6
13 Edinburgh_City 62 837 22.1 111 74.5 12656 11.6 18497.7
14 Eilean_Siar_(Western_Isles) 68.4 599 19.9 117 83.8 8298 15.1 11920.1
15 Falkirk 69.2 680 21.5 121 77.6 9265 13.7 14620
16 Fife 64.7 747 22.5 109 77.9 8314 14.4 16807.5
17 Glasgow_City 75 982 19.4 137 65.3 9483 13.3 19050.8
18 Highland 62.1 719 25.9 109 80.9 8298 14.9 18622.1
19 Inverclyde 67.2 831 18.5 138 80.2 9483 14.6 15373.5
20 Midlothian 67.7 858 19.4 119 84.8 12656 14.3 16645.2
21 Moray 52.7 652 27.2 108 86.4 13566 14.6 17734.4
22 North_Ayrshire 65.7 718 23.7 115 73.5 9483 15 17016.6
23 North_Lanarkshire 72.2 787 20.8 126 74.7 9483 14.9 16369.6
24 Orkney_Islands 47.4 515 26.8 106 87.8 8298 15.3 13802
25 Perth_and_Kinross 51.3 732 23 103 86.6 9611 13.8 16836
26 Renfrewshire 63.6 783 20.5 125 78.5 9483 14.1 16051.5
27 Scottish_Borders_The 50.7 612 23.7 100 80.6 9033 13.3 14504.4
28 Shetland_Islands 51.6 486 23.2 117 84.8 8298 15.9 11275.2
29 South_Ayrshire 56.2 765 23.6 105 79.2 9483 13.7 18054
30 South_Lanarkshire 67.6 793 21.7 125 78.4 9483 14.5 17208.1
31 Stirling 58.9 776 23 110 77.2 9265 13.6 17848
32 West_Dunbartonshire 74.7 978 19.3 130 71.5 9483 15.3 18875.4
33 West_Lothian 67.3 792 21.2 126 82.2 12656 15.1 16790.4

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statsmodels/scikits/statsmodels/datasets/scotland/src/scotland.readme
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#########################################################################################################
# #
# 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.

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<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>&nbsp;&nbsp;</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 &quot;Forfarshire&quot;.
<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 &quot;Orkney Isles&quot;, or &quot;Orkney Islands&quot;,
but NOT &quot;The Orkneys&quot;!
<LI><A NAME="SN6"></A>Includes part of the island of Lewis.
<LI><A NAME="SN7"></A>Also &quot;Shetland Isles&quot;, or &quot;Shetland
Islands&quot;, but NOT &quot;The Shetlands&quot;! Originally known as
&quot;Zetland&quot;.
<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>&copy; 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>
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<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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<html>
<head><title>Scottish Economic Bulletin: Economic Review</title></head>
<body background="/img/backgrn1.gif">
<div align=center>
<table width=70%><tr><td>
<table cellpadding=0 cellspacing=0 width=100% border=0>
<tr><td>
<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>&nbsp;</font></td></tr></table></td></tr>
<tr><td><hr></td></tr>
<tr><td>
<table width=100% cellpadding=2 cellspacing=0>
<tr><td><br>&nbsp;<br></td></tr>
<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>&nbsp;</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 (&pound;&nbsp;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>&nbsp;</TD><TD><b>Value (£&nbsp;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 &#34;pathfinder&#34; 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>&nbsp;</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>&nbsp;
<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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Dataset Name:,"RT331601"
Title:,"Area and population, 1996: Scotland"
Description:,"Area and population, 1996: Scotland
This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998.
"
Source:,"Office for National Statistics; General Register Office for Scotland"
Time Frame:,"1996"
Geographic Coverage:,"United Kingdom"
Universe:,"UK population"
Measure:,"various"
Units:,"See table"
Scalar:,"various"
Formula:,"none"
====================================
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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"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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statsmodels/scikits/statsmodels/datasets/spector/__init__.py
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from data import *

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statsmodels/scikits/statsmodels/datasets/spector/data.py
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"""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

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