R For Marketing Research and Analytics. 2nd ed. 2019

種類:

電子ブック

責任表示:

by Chris Chapman, Elea McDonnell Feit

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2019

著者名:

• Chapman, Chris.
• Feit, Elea McDonnell.
• SpringerLink (Online service)

シリーズ名:

Use R! ;

ISBN:

9783030143169 [3030143163]    

注記:

Chapter 1: Welcom to R -- Chapter 2: An Overview of the R Language -- Chapter 3: Describing Data -- Chapter 4: Relationships Between Continuous Variables -- Chapter 5: Comparing Groups: Tables and Visualizations -- Chapter 6: Comparing Groups: Statistical Tests -- Chapter 7: Identifying Drivers of Outcomes: Linear Models -- Chapter 8: Reducing Data Complexity -- Chapter 9: Assorted Linear Modeling Topics -- Chapter 10: Confirmatory Factor Analysis and Structural Equation Modeling -- Chapter 11: Segmentation: Clustering and Classification -- Chapter 12: Association Rules for Market Basket Analysis -- Chapter 13: Choice Modeling -- Chapter 14: Marketing Mix Models -- Appendix A: R Versions and Related Software -- Appendix B: Scaling Up -- Appendix C: Packages Used -- Appendix D: Online Materials and Data Files.
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 explainin

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