Description
Applied Multivariate Statistics with R, Softcover reprint of the original 1st ed. 2015
Statistics for Biology and Health Series
Author: Zelterman Daniel
Language: EnglishSubjects for Applied Multivariate Statistics with R:
Publication date: 10-2016
Support: Print on demand
Withdrawn from sale
Publication date: 08-2015
393 p. · 15.5x23.5 cm · Hardback
Replaced by new edition: Access to the new edition.
Description
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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.
Daniel Zelterman, PhD, is Professor in the Department of Biostatistics at Yale University. His research areas include computational statistics, models for discrete valued data, and the design of clinical trials in cancer studies. In his spare time he plays oboe and bassoon in amateur orchestral groups and has backpacked hundreds of miles of the Appalachian Trail.
Approach to multivariate statistics for diverse applications that does not require advanced degree in statistics
R is used throughout for statistical analyses and computing, but prior experience with R is not necessary
Contains exercises, full code to carry out the engaging examples, and selected solutions included in an appendix