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＜電子ブック＞
Applied Multivariate Statistics with R / by Daniel Zelterman
(Statistics for Biology and Health. ISSN:21975671)

版	1st ed. 2015.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2015
大きさ	XVI, 393 p. 121 illus., 108 illus. in color : online resource
著者標目	*Zelterman, Daniel author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Epidemiology LCSH:Bioinformatics FREE:Biostatistics FREE:Epidemiology FREE:Bioinformatics FREE:Computational and Systems Biology
一般注記	Introduction -- Elements of R -- Graphical Displays -- Basic Linear Algebra -- The Univariate Normal Distribution -- Bivariate Normal Distribution -- Multivariate Normal Distribution -- Factor Methods -- Multivariate Linear Regression -- Discrimination and Classification -- Clustering -- Time Series Models -- Other Useful Methods -- References -- Appendix -- Selected Solutions -- Index This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. HTTP:URL=https://doi.org/10.1007/978-3-319-14093-3

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