R for Marketing Research and Analytics / by Chris Chapman, Elea McDonnell Feit.

Format:

Book

Author/Creator:

Chapman, Chris, Author.

Feit, Elea McDonnell., Author.

Series:

Use R!, 2197-5744

Language:

English

Subjects (All):

Statistics.

Mathematical statistics--Data processing.

Mathematical statistics.

Marketing.

Statistics in Business, Management, Economics, Finance, Insurance.

Statistics and Computing.

Local Subjects:

Statistics in Business, Management, Economics, Finance, Insurance.

Statistics and Computing.

Marketing.

Physical Description:

1 online resource (459 p.)

Edition:

1st ed. 2015.

Place of Publication:

Springer Nature 2015

Language Note:

English

Summary:

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.

Contents:

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.

Notes:

Description based upon print version of record.

includes bibliographical references and index.

Print version record.

ISBN:

3-319-14436-7