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

**Requirements:** python 2.4. to 2.6 and latest releases of numpy and scipy

**Repository:** http://code.launchpad.net/statsmodels

**Online Documentation:** http://statsmodels.sourceforge.net/


**Installation:**

::

  easy_install scikits.statsmodels

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

::

  setup.py install   or   setup.py develop

**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 as sm
  
  # get data
  nsample = 100
  x = np.linspace(0,10, 100)
  X = sm.tools.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 Content
----------------

.. toctree::
   :maxdepth: 4
   
   
   introduction
   regression
   glm
   rlm
   stattools
   tools
   internal

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

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

