.. statsmodels documentation master file, created by
   sphinx-quickstart on Sat Aug 22 00:38:34 2009.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

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 2.4. to 2.6 and 
  recent releases of numpy (>=1.3) and scipy (>=0.7) 
  earlier versions of numpy and scipy might work but not tested
  Optional: Many of the examples use matplotlib, and some sandbox functions
  have additional dependencies 

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

**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

**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

**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

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


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

.. toctree::
   :maxdepth: 2

   introduction
   related

.. toctree::
   :maxdepth: 3

   regression
   discretemod
   glm
   rlm
   discretemod
   stattools
   tools
   internal
   sandbox

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

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

