Table of Contents

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

About the Author

Conrad Carlberg is a nationally recognized expert on quantitative analysis, data analysis, and management applications such as Microsoft Excel, SAS, and Oracle. He holds a Ph.D. in statistics from the University of Colorado and is a many-time recipient of Microsoft's Excel MVP designation. He is the author of many books, including Business Analysis with Microsoft Excel, Fifth Edition, Statistical Analysis: Microsoft Excel 2016, Regression Analysis Microsoft Excel, and R for Microsoft Excel Users.

Carlberg is a Southern California native. After college he moved to Colorado, where he worked for a succession of startups and attended graduate school. He spent two years in the Middle East, teaching computer science and dodging surly camels. After finishing graduate school, Carlberg worked at US West (a Baby Bell) in product management and at Motorola.

In 1995 he started a small consulting business (www.conradcarlberg.com), which provides design and analysis services to companies that want to guide their business decisions by means of quantitative analysis—approaches that today we group under the term “analytics.” He enjoys writing about those techniques and, in particular, how to carry them out using the world's most popular numeric analysis application, Microsoft Excel.