000			03468cam a22004695i 4500
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005			20210622171442.0
006			m |o d |
007			cr |||||||||||
008			150309s2015 gw |||| o |||| 0|eng
010			_a 2019758167
020			_a9783319144368
024	7		_a10.1007/978-3-319-14436-8 _2doi
035			_a(DE-He213)978-3-319-14436-8
040			_aDLC _beng _epn _erda _cZET-ke
050			_aQA276.45.R43 _b.C43 2015
072		7	_aBUS061000 _2bisacsh
072		7	_aK _2thema
072		7	_aPBT _2bicssc
072		7	_aPBT _2thema
082	0	4	_a330.015195 _223
100	1		_aChapman, Chris, _eauthor.
245	1	0	_aR for Marketing Research and Analytics / _cby Chris Chapman, Elea McDonnell Feit.
250			_a1st ed.
260			_aCham,Switzerland: _bSpringer, _c2015.
300			_a1 online resource (XVIII, 454 pages 108 illustrations, 54 illustrations in color.) _c23 cm
490	1		_aUse R!, _x2197-5736
505	0		_aWelcome to R -- The R Language -- Describing Data -- Relationships Between Continuous Variables -- Comparing Groups: Tables and Visualizations -- Comparing Groups: Statistical Tests -- Identifying Drivers of Outcomes: Linear Models -- Reducing Data Complexity -- Additional Linear Modeling Topics -- Confirmatory Factor Analysis and Structural Equation Modeling -- Segmentation: Clustering and Classification -- Association Rules for Market Basket Analysis -- Choice Modeling -- Conclusion -- Appendix: R Versions and Related Software -- Appendix: Scaling up -- Appendix: Packages Used -- Index.
520			_aThis 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.
650		0	_aMarketing.
650		0	_aStatistics. _969
650	1	4	_aStatistics for Business, Management, Economics, Finance, Insurance.
650	2	4	_aMarketing.
650	2	4	_aStatistics and Computing/Statistics Programs.
700	1		_aFeit, Elea McDonnell, _eauthor.
776	0	8	_iPrint version: _tR for marketing research and analytics _z9783319144351 _w(DLC) 2014960277
776	0	8	_iPrinted edition: _z9783319144351
776	0	8	_iPrinted edition: _z9783319144375
830		0	_aUse R!,
906			_a0 _bibc _corigres _du _encip _f20 _gy-gencatlg
942			_2lcc _cBK _hQA276.45.R43 _kQA276.45.R43 _m.C43 2015
999			_c4956 _d4956