Leverage the full power of Bayesian analysis for competitive advantage

Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and move toward R to do even more.

Step by step, with real-world examples, Carlberg shows you how to use Bayesian analytics to solve a wide array of real problems. Carlberg clarifies terminology that often bewilders analysts, and offers sample R code to take advantage of the rethinking package in R and its gateway to Stan.

As you incorporate these Bayesian approaches into your analytical toolbox, you'll build a powerful competitive advantage for your organization—and yourself.

• Explore key ideas and strategies that underlie Bayesian analysis
• Distinguish prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputs
• Use grid approximation to solve simple univariate problems, and understand its limits as parameters increase
• Perform complex simulations and regressions with quadratic approximation and Richard McElreath's quap function
• Manage text values as if they were numeric
• Learn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC)
• Use MCMC to optimize execution speed in high-complexity problems
• Discover when frequentist methods fail and Bayesian methods are essential—and when to use both in tandem

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.

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