Diving into Statsmodels with an Intro to Python & Pydata by Skipper Seabold.
From the post:
Abhijit and Marck, the organizers of Statistical Programming DC, kindly invited me to give the talk for the April meetup on statsmodels. Statsmodels is a Python module for conducting data exploration and statistical analysis, modeling, and inference. You can find many common usage examples and a full list of features in the online documentation.
For those who were unable to make it, the entire talk is available as an IPython Notebook on github. If you aren’t familiar with the notebook, it is an incredibly useful and exciting tool. The Notebook is a web-based interactive document that allows you combine text, mathematics, graphics, and code (languages other than Python such as R, Julia, Matlab, and, even, C/C++ and Fortran are supported).
The talk introduced users to what is available in statsmodels. Then we looked at a typical statsmodels workflow, highlighting high-level features such as our integration with pandas and the use of formulas via patsy. We covered a few areas in a little more detail building off some of our example datasets. And finally we discussed some of the features we have in the pipeline for our upcoming release.
I don’t know that this will help in public policy debates but it can’t hurt to have your own analysis of available data.
Of course the key to “your own analysis” is having the relevant data before meetings/discussions, etc. Request and document your request for relevant data long prior to public meetings. If you don’t get the data, be sure to get your prior request documented in the meeting record.