
If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.
You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

Develop your understanding of probability and statistics by writing and testing code

Run experiments to test statistical behavior, such as generating samples from several distributions

Use simulations to understand concepts that are hard to grasp mathematically

Learn topics not usually covered in an introductory course, such as Bayesian estimation

Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools

Use statistical inference to answer questions about realworld data



Analytical Geometry of Three Dimensions (Dover Books on Mathematics)
Brief but rigorous, this text is geared toward advanced undergraduates and graduate students. It covers the coordinate system, planes and lines, spheres, homogeneous coordinates, general equations of the second degree, quadric in Cartesian coordinates, and intersection of quadrics.
Mathematician, physicist, and astronomer, William...     Levine's Guide to SPSS for Analysis of Variance: Second EditionIn the decade since the publication of the first edition of this guide (Levine, 1991), and despite the development of several more specialized statistical techniques, analysis of variance (ANOVA) continues to be the workhorse for many behavioral science researchers. This guide provides instructions and examples for running analyses of variance, as... 
