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R for marketing research and analytics

Chapman, Chris.

R for marketing research and analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	R for marketing research and analyticsby Chris Chapman, Elea McDonnell Feit.
Author:	Chapman, Chris.
other author:	Feit, Elea McDonnell.
Published:	Cham :Springer International Publishing :2015.
Description:	xviii, 454 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	R (Computer program language)
Online resource:	http://dx.doi.org/10.1007/978-3-319-14436-8
ISBN:	9783319144368 (electronic bk.)

R for marketing research and analytics
Chapman, Chris.

R for marketing research and analytics[electronic resource] /by Chris Chapman, Elea McDonnell Feit. - Cham :Springer International Publishing :2015. - xviii, 454 p. :ill. (some col.), digital ;24 cm. - Use R!,2197-5736. - Use R!.

Welcome to R -- The R Language -- Describing Data -- Relationships Between Continuous Variables -- Comparing Groups: Tables and Visualizations -- Comparing Groups: Statistical Tests -- Identifying Drivers of Outcomes: Linear Models -- Reducing Data Complexity -- Additional Linear Modeling Topics -- Confirmatory Factor Analysis and Structural Equation Modeling -- Segmentation: Clustering and Classification -- Association Rules for Market Basket Analysis -- Choice Modeling -- Conclusion -- Appendix: R Versions and Related Software -- Appendix: Scaling up -- Appendix: Packages Used -- Index.

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.

ISBN: 9783319144368 (electronic bk.)

Standard No.: 10.1007/978-3-319-14436-8doiSubjects--Topical Terms:

210846
R (Computer program language)

LC Class. No.: QA276.45.R3

Dewey Class. No.: 005.133

R for marketing research and analytics
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245 1 0 $a R for marketing research and analytics $h [electronic resource] / $c by Chris Chapman, Elea McDonnell Feit.
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300 $a xviii, 454 p. : $b ill. (some col.), digital ; $c 24 cm.
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505 0 $a Welcome to R -- The R Language -- Describing Data -- Relationships Between Continuous Variables -- Comparing Groups: Tables and Visualizations -- Comparing Groups: Statistical Tests -- Identifying Drivers of Outcomes: Linear Models -- Reducing Data Complexity -- Additional Linear Modeling Topics -- Confirmatory Factor Analysis and Structural Equation Modeling -- Segmentation: Clustering and Classification -- Association Rules for Market Basket Analysis -- Choice Modeling -- Conclusion -- Appendix: R Versions and Related Software -- Appendix: Scaling up -- Appendix: Packages Used -- Index.
520 $a 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.
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