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.
Statsmodels is participating for the fourth time in GSoC under the umbrella of the Python Software Foundation. We have set up a wiki page with ideas for projects. Feel free to contribute to the ideas page or contact the mailing list if you are interested in applying so we can coordinate on developing your application and project - the earlier the better.
Get the data, run the estimation, and look at the results. For example, here is a minimal ordinary least squares example
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.