Doing Bayesian Data Analysis: A Tutorial with R and BUGS PDF Download Ebook. John K. Kruschke provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data.

The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience.

From the very first chapter, the engaging writing style will get readers excited about this topic, a comment one can rarely make about statistical books. Clearly a master teacher, the author, John Kruschke, uses plain language to explain complex ideas and concepts. A comprehensive website is associated with the book and provides program codes, examples, data, and solutions to the exercises. If the book is used to teach a statistics course, this set of materials will be necessary and helpful for students as they go through the materials in the book step by step.

This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. Free software now includes programs in JAGS, which runs on Macintosh, Linux, and Windows.

It is accessible, including the basics of essential concepts of probability and random sampling with examples with R programming language and BUGS software. Comprehensive coverage of all scenarios is addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis).

This text also offers coverage of experiment planning, R and BUGS computer programming code on website. Exercises have explicit purposes and guidelines for accomplishment. The text has the potential to change the methodological toolbox of a new generation of social scientists, bringing them up to a level of computation, modeling, and analysis that they might not have thought to be within their grasp. Where past approaches to teaching statistics to those in psychology and economics have not lead to widespread insight, this tutorial approach might.

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