Preface Chapter 1 Bayesian Analysis and R: An Overview Bayes Comes Back About Structuring Priors Watching the Jargon Priors, Likelihoods, and Posteriors The Prior The Likelihood Contrasting a Frequentist Analysis with a Bayesian The Frequentist Approach The Bayesian Approach Summary Chapter 2 Generating Posterior Distributions with the Binomial Distribution Understanding the Binomial Distribution Understanding Some Related Functions Working with R's Binomial Functions Using R's dbinom Function Using R's pbinom Function Using R's qbinom Function Using R's rbinom Function Grappling with the Math Summary Chapter 3 Understanding the Beta Distribution Establishing the Beta Distribution in Excel Comparing the Beta Distribution with the Binomial Distribution Decoding Excel's Help Documentation for BETA.DIST Replicating the Analysis in R Understanding dbeta Understanding pbeta Understanding qbeta About Confidence Intervals Applying qbeta to Confidence Intervals Applying BETA.INV to Confidence Intervals Summary Chapter 4 Grid Approximation and the Beta Distribution More on Grid Approximation Setting the Prior Using the Results of the Beta Function Tracking the Shape and Location of the Distribution Inventorying the Necessary Functions Looking Behind the Curtains Moving from the Underlying Formulas to the Functions Comparing Built-in Functions with Underlying Formulas Understanding Conjugate Priors Summary Chapter 5 Grid Approximation with Multiple Parameters Setting the Stage Global Options Local Variables Specifying the Order of Execution Normal Curves, Mu and Sigma Visualizing the Arrays Combining Mu and Sigma Putting the Data Together Calculating the Probabilities Folding in the Prior Inventorying the Results Viewing the Results from Different Perspectives Summary Chapter 6 Regression Using Bayesian Methods Regression a la Bayes Sample Regression Analysis Matrix Algebra Methods Understanding quap Continuing the Code A Full Example Designing the Multiple Regression Arranging a Bayesian Multiple Regression Summary Chapter 7 Handling Nominal Variables Using Dummy Coding Supplying Text Labels in Place of Codes Comparing Group Means Summary Chapter 8 MCMC Sampling Methods Quick Review of Bayesian Sampling Grid Approximation Quadratic Approximation MCMC Gets Up To Speed A Sample MCMC Analysis ulam's Output Validating the Results Getting Trace Plot Charts Summary and Concluding Thoughts Appendix Installation Instructions for RStan and the rethinking Package on the Windows Platform Glossary Downloadable Bonus Content Excel Worksheets Book: Statistical Analysis: Microsoft Excel 2016 (PDF) 9780137580989 TOC 10/24/2022.