R For Marketing Research and Analytics

Business & Finance, Economics, Statistics, Nonfiction, Computers, Application Software, General Computing
Cover of the book R For Marketing Research and Analytics by Chris Chapman, Elea McDonnell  Feit, Springer International Publishing
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Author: Chris Chapman, Elea McDonnell Feit ISBN: 9783030143169
Publisher: Springer International Publishing Publication: March 28, 2019
Imprint: Springer Language: English
Author: Chris Chapman, Elea McDonnell Feit
ISBN: 9783030143169
Publisher: Springer International Publishing
Publication: March 28, 2019
Imprint: Springer
Language: English

The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book* *is 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. 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book* *is 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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