04988nam a22006855i 4500 000717966 DE-He213 20240923053748.0 20240322035452.0 cr nn 008mamaa 190328s2019 sz | s |||| 0|eng d NEZPRACOVANÝ IMPORT 9783030143169 978-3-030-14316-9 10.1007/978-3-030-14316-9 doi QA276.4-.45 PBT bicssc UFM bicssc COM077000 bisacsh PBT thema UFM thema 519.5 23 Chapman, Chris. author. aut http://id.loc.gov/vocabulary/relators/aut R For Marketing Research and Analytics [elektronický zdroj] / by Chris Chapman, Elea McDonnell Feit. 2nd ed. 2019. Cham : Springer International Publishing : Imprint: Springer, 2019. XX, 487 p. 151 illus., 69 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Use R!, 2197-5744 Chapter 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. 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 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. . elektronické knihy fd186907 czenas electronic books eczenas Mathematical statistics Data processing. Statistics . Marketing. Statistics and Computing. Statistics in Business, Management, Economics, Finance, Insurance. Marketing. Feit, Elea McDonnell. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) Springer Nature eBook Printed edition: 9783030143152 Printed edition: 9783030143176 Printed edition: 9783030835644 Use R!, 2197-5744 https://zdroje.vse.cz/login?url=https://doi.org/10.1007/978-3-030-14316-9 Plný text SPRINGER-OWNED EB PhDinfra PAR 000683779 UEP01 776 E-book Tištěná verze NEPOSILAT EB ZDB-2-SMA ZDB-2-SXMS Mathematics and Statistics (SpringerNature-11649) Mathematics and Statistics (R0) (SpringerNature-43713)