| 000 | 02462nam a22002417a 4500 | ||
|---|---|---|---|
| 999 |
_c2821 _d2821 |
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| 005 | 20220719121424.0 | ||
| 008 | 220719b ||||| |||| 00| 0 eng d | ||
| 020 | _a9783030143152 | ||
| 082 |
_a005.133 _bCHA |
||
| 100 |
_aChapman, Chris _97756 |
||
| 245 | _aR for marketing research and analytics | ||
| 250 | _a2nd | ||
| 260 |
_bSpringer _aSwitzerland _c2019 |
||
| 300 | _axx, 487 p. | ||
| 365 |
_aEURO _b69.99 |
||
| 520 | _aAbout this book 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. | ||
| 650 |
_aMarketing research--Statistical methods _97757 |
||
| 650 |
_aR (Computer program language) _91512 |
||
| 650 |
_aMarketing _9209 |
||
| 650 |
_aMathematical statistics _9837 |
||
| 700 |
_aFeit, Elea McDonnell _97758 |
||
| 942 |
_2ddc _cBK |
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