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Applied Multivariate Statistics with R, by Daniel Zelterman
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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.
- Sales Rank: #5768402 in Books
- Published on: 2016-11-11
- Released on: 2016-11-11
- Original language: English
- Dimensions: 9.25" h x .97" w x 6.10" l,
- Binding: Paperback
- 412 pages
Review
“This book demonstrates the process and outcomes for a wide array of multivariate statistical applications using program R. … The chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. This book is strongly recommended for graduate-level statistics practitioners.” (Hemang B. Panchal, Doody’s Book Reviews, December, 2015)
From the Back Cover
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.
About the Author
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.
Most helpful customer reviews
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Practical and Helpful
By Beth N
Dr. Zelterman has a knack for bringing real-world relevance to more complex statistical concepts. This book combines interesting (and sometimes fun) examples of multivariate statistical problems with practical examples in the use of the free-to-use software, R. Beginning statisticians as well as more advanced users will find helpful examples and techniques within.
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