Welcome to statsmodels’s documentation!

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

Indices and tables

Table Of Contents

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