## Chapter 7. Descriptive Statistics

This and the following chapter are mainly aimed at SAS, SPSS, or Minitab users, and especially those employing the languages R or S for statistical computing. We will develop an environment for working effectively in the field of data analysis, with the aid of IPython sessions powered up with the following resources from the SciPy stack:

The probability and statistics submodule of the library of symbolic computations, `sympy.stats`

.

The two libraries of statistical functions `scipy.stats`

and `scipy.stats.mstats`

(the latter for data provided by masked arrays), together with the module `statsmodels`

, for data exploration, estimation on statistical models, and performing statistical tests in a numerical setting. The package `statsmodels`

uses, under the hood, the powerful library `patsy`

to describe statistical models and building design matrices in Python (R or S users will find `patsy`

compatible with their formula mini-language).

For statistical inference, we again use...