.. :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 available 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 `statsmodels.org `__.
Minimal Examples
----------------
Since version ``0.5.0`` of ``statsmodels``, you can use R-style formulas
together with ``pandas`` data frames to fit your models. Here is a simple
example using ordinary least squares:
.. ipython:: python
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
# Load data
dat = sm.datasets.get_rdataset("Guerry", "HistData").data
# Fit regression model (using the natural log of one of the regressors)
results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()
# Inspect the results
print(results.summary())
You can also use ``numpy`` arrays instead of formulas:
.. ipython:: python
import numpy as np
import statsmodels.api as sm
# Generate artificial data (2 regressors + constant)
nobs = 100
X = np.random.random((nobs, 2))
X = sm.add_constant(X)
beta = [1, .1, .5]
e = np.random.random(nobs)
y = np.dot(X, beta) + e
# Fit regression model
results = sm.OLS(y, X).fit()
# Inspect 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.
Citation
--------
When using statsmodels in scientific publication, please consider using the
following citation:
Seabold, Skipper, and Josef Perktold. "`Statsmodels: Econometric and statistical modeling with python. `_" *Proceedings of the 9th Python in Science Conference.* 2010.
Bibtex entry::
@inproceedings{seabold2010statsmodels,
title={Statsmodels: Econometric and statistical modeling with python},
author={Seabold, Skipper and Perktold, Josef},
booktitle={9th Python in Science Conference},
year={2010},
}
Basic Documentation
-------------------
.. toctree::
:maxdepth: 3
release/index
gettingstarted
example_formulas
install
about
Information about the structure and development of
statsmodels:
.. toctree::
:maxdepth: 1
endog_exog
importpaths
pitfalls
dev/index
dev/internal
Table of Contents
-----------------
.. toctree::
:maxdepth: 3
regression
glm
gee
rlm
mixed_linear
discretemod
mixed_glm
anova
tsa
statespace
vector_ar
duration
stats
nonparametric
gmm
contingency_tables
imputation
multivariate
emplike
miscmodels
distributions
graphics
iolib
tools
datasets/index
sandbox
Indices and tables
------------------
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`