<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 |
本文言語 | 英語 |
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 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
Springer eBooks | 9783319140933 |
|
電子リソース |
|
EB00237058 |
書誌詳細を非表示
データ種別 | 電子ブック |
---|---|
分 類 | LCC:QH323.5 DC23:57,015,195 |
書誌ID | 4000115037 |
ISBN | 9783319140933 |
類似資料
この資料の利用統計
このページへのアクセス回数:3回
※2017年9月4日以降