Bayesian Analysis with Excel and R

Häftad, Engelska, 2023

343 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

Beskrivning

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

Produktinformation

Utgivningsdatum:2023-01-30
Mått:176 x 230 x 11 mm
Vikt:319 g
Format:Häftad
Språk:Engelska
Antal sidor:192
Upplaga:1
Förlag:Pearson Education
ISBN:9780137580989

Utforska kategorier

Mer om författaren

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.

Innehållsförteckning

PrefaceChapter 1 Bayesian Analysis and R: An OverviewBayes Comes BackAbout Structuring PriorsWatching the JargonPriors, Likelihoods, and PosteriorsThe PriorThe LikelihoodContrasting a Frequentist Analysis with a BayesianThe Frequentist ApproachThe Bayesian ApproachSummaryChapter 2 Generating Posterior Distributions with the Binomial DistributionUnderstanding the Binomial DistributionUnderstanding Some Related FunctionsWorking with R's Binomial FunctionsUsing R's dbinom FunctionUsing R's pbinom FunctionUsing R's qbinom FunctionUsing R's rbinom FunctionGrappling with the Math SummaryChapter 3 Understanding the Beta DistributionEstablishing the Beta Distribution in ExcelComparing the Beta Distribution with the Binomial DistributionDecoding Excel's Help Documentation for BETA.DISTReplicating the Analysis in RUnderstanding dbetaUnderstanding pbetaUnderstanding qbetaAbout Confidence IntervalsApplying qbeta to Confidence IntervalsApplying BETA.INV to Confidence IntervalsSummaryChapter 4 Grid Approximation and the Beta DistributionMore on Grid ApproximationSetting the PriorUsing the Results of the Beta FunctionTracking the Shape and Location of the DistributionInventorying the Necessary FunctionsLooking Behind the CurtainsMoving from the Underlying Formulas to the FunctionsComparing Built-in Functions with Underlying FormulasUnderstanding Conjugate Priors SummaryChapter 5 Grid Approximation with Multiple ParametersSetting the StageGlobal OptionsLocal VariablesSpecifying the Order of ExecutionNormal Curves, Mu and SigmaVisualizing the ArraysCombining Mu and SigmaPutting the Data TogetherCalculating the ProbabilitiesFolding in the PriorInventorying the ResultsViewing the Results from Different PerspectivesSummaryChapter 6 Regression Using Bayesian MethodsRegression a la BayesSample Regression AnalysisMatrix Algebra MethodsUnderstanding quapContinuing the CodeA Full ExampleDesigning the Multiple RegressionArranging a Bayesian Multiple RegressionSummaryChapter 7 Handling Nominal VariablesUsing Dummy Coding Supplying Text Labels in Place of CodesComparing Group MeansSummaryChapter 8 MCMC Sampling MethodsQuick Review of Bayesian SamplingGrid ApproximationQuadratic ApproximationMCMC Gets Up To SpeedA Sample MCMC Analysisulam's OutputValidating the ResultsGetting Trace Plot ChartsSummary and Concluding ThoughtsAppendix Installation Instructions for RStan and the rethinking Package on the Windows PlatformGlossary Downloadable Bonus ContentExcel Worksheets Book: Statistical Analysis: Microsoft Excel 2016 (PDF) 9780137580989 TOC 10/24/2022