9783030143152-3030143155-R For Marketing Research and Analytics (Use R!)

R For Marketing Research and Analytics (Use R!)

ISBN-13: 9783030143152
ISBN-10: 3030143155
Edition: 2nd ed. 2019
Author: Chris Chapman, Elea McDonnell Feit
Publication date: 2019
Publisher: Springer
Format: Paperback 507 pages
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ISBN-13: 9783030143152
ISBN-10: 3030143155
Edition: 2nd ed. 2019
Author: Chris Chapman, Elea McDonnell Feit
Publication date: 2019
Publisher: Springer
Format: Paperback 507 pages

Summary

R For Marketing Research and Analytics (Use R!) (ISBN-13: 9783030143152 and ISBN-10: 3030143155), written by authors Chris Chapman, Elea McDonnell Feit, was published by Springer in 2019. With an overall rating of 4.5 stars, it's a notable title among other Econometrics & Statistics (Economics, Statistics, Education & Reference, Mathematical & Statistical, Software) books. You can easily purchase or rent R For Marketing Research and Analytics (Use R!) (Paperback) from BooksRun, along with many other new and used Econometrics & Statistics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $14.41.

Description

The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This bookis a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.

Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis.

With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.

The 2nd edition increases the book’s utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.

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