.. statsmodels documentation master file, created by
   sphinx-quickstart on Sat Aug 22 00:38:34 2009.
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.. :tocdepth: 2

Welcome to statsmodels's documentation!
=======================================

:mod:`scikits.statsmodels` is a pure python package that provides classes and
functions for the estimation of several categories of statistical models. These
currently include linear regression models, OLS, GLS, WLS and GLS with AR(p)
errors, generalized linear models for six distribution families and
M-estimators for robust linear models. An extensive list of result statistics
are avalable for each estimation problem

Quickstart for the impatient
----------------------------

**License:** Simplified BSD

**Requirements:** python >= 2.5, tested with 2.5., 2.6, 2.7 and 3.2
  recent releases of numpy (>=1.4) and scipy (>=0.7)
  (tested with numpy 1.4.1, 1.5.1 and 1.6.0, scipy 0.7.2, 0.8.0, 0.9.0)
  Optional: Many of the examples use matplotlib, and some sandbox functions
  have additional dependencies

**Repository:** http://code.launchpad.net/statsmodels
(will move to github)

**Documentation:** http://statsmodels.sourceforge.net/
    and in the docs/build folder

**Pypi:** http://pypi.python.org/pypi/scikits.statsmodels

**Mailing List:** http://groups.google.com/group/pystatsmodels?hl=en

**Bug Tracker:**  https://bugs.launchpad.net/statsmodels
(might change)

**Development:** See our :doc:`developer's page <developernotes>`.

**Installation:**

::

  easy_install scikits.statsmodels

or get the source from pypi, sourceforge, or from the launchpad repository and

::

  setup.py install  or, if this does not work, try
  setup.py build install

**Note:**
Due to our infrequent official releases, we want to point out that the trunk
branch in the public repository will have the most recent code and is
usually stable, tested, and fine for daily use.

**Usage:**

Get the data, run the estimation, and look at the results.
For example, here is a minimal ordinary least squares case ::

  import numpy as np
  import scikits.statsmodels.api as sm

  # get data
  nsample = 100
  x = np.linspace(0,10, 100)
  X = sm.add_constant(np.column_stack((x, x**2)))
  beta = np.array([1, 0.1, 10])
  y = np.dot(X, beta) + np.random.normal(size=nsample)

  # run the regression
  results = sm.OLS(y, X).fit()

  # look at the results
  print results.summary()

  and look at `dir(results)` to see some of the results
  that are available


Table of Contents
-----------------

.. toctree::
   :maxdepth: 1

   introduction
   related
   developernotes

.. toctree::
   :maxdepth: 2

   regression
   glm
   rlm
   discretemod
   tsa
   stats
   tools
   miscmodels
   internal
   gmm
   distributions
   datasets
   sandbox


Indices and tables
------------------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

