LEADER 04398nam 22005535 450 001 9910338256503321 005 20251113211421.0 010 $a3-030-14316-3 024 7 $a10.1007/978-3-030-14316-9 035 $a(CKB)4930000000042107 035 $a(MiAaPQ)EBC5742519 035 $a(DE-He213)978-3-030-14316-9 035 $a(PPN)23523222X 035 $a(EXLCZ)994930000000042107 100 $a20190328d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aR For Marketing Research and Analytics /$fby Chris Chapman, Elea McDonnell Feit 205 $a2nd ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (492 pages) 225 1 $aUse R!,$x2197-5744 311 08$a3-030-14315-5 327 $aChapter 1: Welcom to R -- Chapter 2: An Overview of the R Language -- Chapter 3: Describing Data -- Chapter 4: Relationships Between Continuous Variables -- Chapter 5: Comparing Groups: Tables and Visualizations -- Chapter 6: Comparing Groups: Statistical Tests -- Chapter 7: Identifying Drivers of Outcomes: Linear Models -- Chapter 8: Reducing Data Complexity -- Chapter 9: Assorted Linear Modeling Topics -- Chapter 10: Confirmatory Factor Analysis and Structural Equation Modeling -- Chapter 11: Segmentation: Clustering and Classification -- Chapter 12: Association Rules for Market Basket Analysis -- Chapter 13: Choice Modeling -- Chapter 14: Marketing Mix Models -- Appendix A: R Versions and Related Software -- Appendix B: Scaling Up -- Appendix C: Packages Used -- Appendix D: Online Materials and Data Files. 330 $aThe 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian modelswith a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. The 2nd edition increases the book?s utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code. . 410 0$aUse R!,$x2197-5744 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aMarketing 606 $aStatistics and Computing 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aMarketing 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 0$aMarketing. 615 14$aStatistics and Computing. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aMarketing. 676 $a519.502855133 676 $a005.133 700 $aChapman$b Chris$4aut$4http://id.loc.gov/vocabulary/relators/aut$019284 702 $aFeit$b Elea McDonnell$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910338256503321 996 $aR for Marketing Research and Analytics$92512379 997 $aUNINA