R For Marketing Research and Analytics

Szerző: Chris Chapman, Elanor Feit

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer Nature Switzerland AG

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

32 901 Ft

The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R...

Információk a könyvről

Szerző

Chris Chapman, Elanor Feit

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2019

oldal

487

EAN

9783030143152

Enbook ID

21298404

Kiadó

Springer Nature Switzerland AG

Súly

771

Méretek

155 x 235 x 28

Teljes leírás

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 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. The 2nd edition increases the book's utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.

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R For Marketing Research and Analytics

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