Bayesian Analysis with Excel and R

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

Table of Contents

1 Bayesian Analysis and R: An Overview

2 Generating Posterior Distributions with the Binomial Dist ribution

3 Understanding the Beta Dist ribution

4 Grid Approximation and the Beta Distribution

5 Grid Approximation with Multiple Parameters

6 Regression Using Bayesian Methods

7 Handling Nominal Variables

8 MCMC Sampling Methods

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.