๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

R for Marketing Research and Analytics

โœ Scribed by Chris Chapman, Elea McDonnell Feit (auth.)


Publisher
Springer International Publishing
Year
2015
Tongue
English
Leaves
459
Series
Use R!
Edition
1
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


This bookis 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.

โœฆ Table of Contents


Front Matter....Pages i-xviii
Front Matter....Pages 1-1
Welcome to R....Pages 3-10
An Overview of the R Language....Pages 11-44
Front Matter....Pages 45-45
Describing Data....Pages 47-75
Relationships Between Continuous Variables....Pages 77-109
Comparing Groups: Tables and Visualizations....Pages 111-133
Comparing Groups: Statistical Tests....Pages 135-157
Identifying Drivers of Outcomes: Linear Models....Pages 159-191
Front Matter....Pages 193-193
Reducing Data Complexity....Pages 195-223
Additional Linear Modeling Topics....Pages 225-266
Confirmatory Factor Analysis and Structural Equation Modeling....Pages 267-298
Segmentation: Clustering and Classification....Pages 299-338
Association Rules for Market Basket Analysis....Pages 339-361
Choice Modeling....Pages 363-400
Back Matter....Pages 401-454

โœฆ Subjects


Statistics for Business/Economics/Mathematical Finance/Insurance; Statistics and Computing/Statistics Programs; Marketing


๐Ÿ“œ SIMILAR VOLUMES


R for Marketing Research and Analytics
โœ Chris Chapman, Elea McDonnell Feit ๐Ÿ“‚ Library ๐Ÿ“… 2019 ๐Ÿ› Springer ๐ŸŒ English

We are here to help you learn R for marketing research and analytics. R is a great choice for marketing analysts. It offers unsurpassed capabilities for fitting statistical models. It is extensible and able to process data from many different systems, in a variety of forms, for both small and lar

R For Marketing Research and Analytics
โœ Chris Chapman; Elea McDonnell Feit ๐Ÿ“‚ Library ๐Ÿ“… 2019 ๐Ÿ› Springer ๐ŸŒ English

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 minima

R for Marketing Research and Analytics
โœ Chapman, Chris;Feit, Elea McDonnell ๐Ÿ“‚ Library ๐Ÿ“… 2019 ๐Ÿ› Springer International Publishing ๐ŸŒ English

"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 minim

Python for Marketing Research and Analyt
โœ Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit ๐Ÿ“‚ Library ๐Ÿ“… 2020 ๐Ÿ› Springer International Publishing;Springer ๐ŸŒ English

<p><p>This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all ana