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R for Marketing Research and Analytics

  • Chris Chapman
  • Elea McDonnell Feit

Part of the Use R! book series (USE R)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Basics of R

    1. Front Matter
      Pages 1-1
    2. Chris Chapman, Elea McDonnell Feit
      Pages 3-10
    3. Chris Chapman, Elea McDonnell Feit
      Pages 11-44
  3. Fundamentals of Data Analysis

    1. Front Matter
      Pages 45-45
    2. Chris Chapman, Elea McDonnell Feit
      Pages 47-75
    3. Chris Chapman, Elea McDonnell Feit
      Pages 77-109
    4. Chris Chapman, Elea McDonnell Feit
      Pages 111-133
    5. Chris Chapman, Elea McDonnell Feit
      Pages 135-157
    6. Chris Chapman, Elea McDonnell Feit
      Pages 159-191
  4. Advanced Marketing Applications

    1. Front Matter
      Pages 193-193
    2. Chris Chapman, Elea McDonnell Feit
      Pages 195-223
    3. Chris Chapman, Elea McDonnell Feit
      Pages 225-266
    4. Chris Chapman, Elea McDonnell Feit
      Pages 267-298
    5. Chris Chapman, Elea McDonnell Feit
      Pages 299-338
    6. Chris Chapman, Elea McDonnell Feit
      Pages 339-361
    7. Chris Chapman, Elea McDonnell Feit
      Pages 363-400
  5. Back Matter
    Pages 401-454

About this book

Introduction

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.

Keywords

Marketing analysis Marketing applications Marketing data analysis Marketing research Quantitative marketing R language R packages for marketing applications Visualization

Authors and affiliations

  • Chris Chapman
    • 1
  • Elea McDonnell Feit
    • 2
  1. 1.Google, Inc.SeattleUSA
  2. 2.LeBow College of BusinessDrexel UniversityPhiladelphiaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-14436-8
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-14435-1
  • Online ISBN 978-3-319-14436-8
  • Series Print ISSN 2197-5736
  • Series Online ISSN 2197-5744
  • Buy this book on publisher's site