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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. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan 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, correlation, multiple regression, and chi-square (contingency table analysis). Included are step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs. 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. Knowledge of algebra and basic calculus is a prerequisite. Accessible, including the basics of essential concepts of probability and random samplingExamples with R programming language and JAGS softwareComprehensive 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)Coverage of experiment planningR and JAGS computer programming code on websiteExercises have explicit purposes and guidelines for accomplishment Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs. ÍNDICE: 1.) This Book's Organization: Read Me First! Part 1.) The Basics: Parameters, Probability, Bayes' Rule, and R 2.) Introduction: Models We Believe In 3.) What Is This Stuff Called Probability? 4.) Bayes' Rule Part 2.) All the Fundamentals Applied to Inferring a Binomial Proportion 5.) Inferring a Binomial Proportion via Exact Mathematical Analysis 6.) Inferring a Binomial Proportion via Grid Approximation 7.) Inferring a Binomial Proportion via the Metropolis Algorithm 8.) Inferring Two Binomial Proportions via Gibbs Sampling 9.) Bernoulli Likelihood with Hierarchical Prior 10.) Hierarchical Modeling and Model Comparison 11.) Null Hypothesis Significance Testing 12.) Bayesian Approaches to Testing a Point (Null) Hypothesis 13.) Goals, Power, and Sample Size Part 3.) Applied to the Generalized Linear Model 14.) Overview of the Generalized Linear Model 15.) Metric Predicted Variable on a Single Group 16.) Metric Predicted Variable with One Metric Predictor 17.) Metric Predicted Variable with Multiple Metric Predictors 18.) Metric Predicted Variable with One Nominal Predictor 19.) Metric Predicted Variable with Mult