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Published by Addison-Wesley Professional (January 26, 2023) © 2023

Conrad Carlberg
    VitalSource eTextbook ( Lifetime access )
    €34,99
    ISBN-13: 9780137580897

    Bayesian Analysis with Excel and R ,1st edition

    Published 2023

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    Language: English

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    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

    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

     

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