R for Marketing Research and Analytics / Chris Chapman.
Material type:
TextLanguage: English Series: Use RPublisher: Cham Heidelberg : Springer, 2015 & 2019Edition: 1st & 2nd edDescription: 1 online resource (xviii, 454 pages) : illustrations (some color)ISBN: 9783319144351Subject(s): R (Computer program language) | Marketing research -- Statistical methods | Mathematical & statistical softwareDDC classification: 658.83 Online resources: Click here to access online | Click here to access online | Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Books | Institute of Public Enterprise, Library S Campus | 658.83 CHA.R (Browse shelf) | Available | 45925 | |
| Books | Institute of Public Enterprise, Library S Campus | 658.83 CHA.R (Browse shelf) | Available | 45926 | |
| Books | Institute of Public Enterprise, Library S Campus | 658.83 CHA.R (Browse shelf) | Available | 42064 |
Includes bibliographical references and indexes.
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.
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