Methods of multivariate analysis

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Title:

Author:

Rencher, Alvin C., 1934-

ISBN:

9780471418894

Personal Author:

Rencher, Alvin C., 1934-

Edition:

2nd ed.

Publication Information:

New York : J. Wiley, 2002.

Physical Description:

xxii, 708 p. : ill. ; 24 cm.

Series:

Wiley series in probability and mathematical statistics

Series Title:

Wiley series in probability and mathematical statistics

General Note:

"A Wiley-Interscience publication."

Contents:

1. Introduction -- 2. Matrix Algebra -- 3. Characterizing and Displaying Multivariate Data -- 4. The Multivariate Normal Distribution -- 5. Tests on One or Two Mean Vectors -- 6. Multivariate Analysis of Variance -- 7. Tests on Covariance Matrices -- 8. Discriminant Analysis: Description of Group Separation -- 9. Classification Analysis: Allocation of Observations to Groups -- 10. Multivariate Regression -- 11. Canonical Correlation -- 12. Principal Component Analysis -- 13. Factor Analysis -- 14. Cluster Analysis -- 15. Graphical Procedures -- C. Data Sets and SAS Files.

Subject Term:

Multivariate analysis.

Electronic Access:

Table of contents http://www.loc.gov/catdir/toc/wiley021/2001046735.html
Publisher description http://www.loc.gov/catdir/description/wiley0310/2001046735.html

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168015733559	519.535 R396M 2002	1	Searching... Unknown

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Summary

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Methods of Multivariate Analysis was among those chosen.

When measuring several variables on a complex experimental unit, it is often necessary to analyze the variables simultaneously, rather than isolate them and consider them individually. Multivariate analysis enables researchers to explore the joint performance of such variables and to determine the effect of each variable in the presence of the others. The Second Edition of Alvin Rencher's Methods of Multivariate Analysis provides students of all statistical backgrounds with both the fundamental and more sophisticated skills necessary to master the discipline.

To illustrate multivariate applications, the author provides examples and exercises based on fifty-nine real data sets from a wide variety of scientific fields. Rencher takes a "methods" approach to his subject, with an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. The Second Edition contains revised and updated chapters from the critically acclaimed First Edition as well as brand-new chapters on:

Cluster analysis Multidimensional scaling Correspondence analysis Biplots Each chapter contains exercises, with corresponding answers and hints in the appendix, providing students the opportunity to test and extend their understanding of the subject. Methods of Multivariate Analysis provides an authoritative reference for statistics students as well as for practicing scientists and clinicians.