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.. :tocdepth: 2
Welcome to Statsmodels's Documentation
======================================
:mod:`statsmodels` is a Python module that provides classes and functions for the estimation
of many different statistical models, as well as for conducting statistical tests, and statistical
data exploration. An extensive list of result statistics are avalable for each estimator.
The results are tested against existing statistical packages to ensure that they are correct. The
package is released under the open source Modified BSD (3-clause) license. The online documentation
is hosted at `sourceforge <http://statsmodels.sourceforge.net/>`__.
Getting Started
---------------
Get the data, run the estimation, and look at the results.
For example, here is a minimal ordinary least squares example
.. code-block:: python
import numpy as np
import 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()
Have a look at `dir(results)` to see available results. Attributes are
described in `results.__doc__` and results methods have their own docstrings.
Table of Contents
-----------------
.. toctree::
:maxdepth: 1
introduction
related
dev/index
pitfalls
importpaths
.. toctree::
:maxdepth: 2
regression
glm
rlm
discretemod
tsa
stats
nonparametric
tools
miscmodels
dev/internal
gmm
distributions
graphics
iolib
datasets/index
sandbox
Related Projects
----------------
See our :ref:`related projects page <related>`.
Indices and tables
------------------
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
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