Rencher Methods of Multivariate Analysis

2. Auflage 2003
ISBN: 978-0-471-46172-2
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, Band 1, 738 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. Methods ofMultivariate 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 jointperformance of such variables and to determine the effect of eachvariable in the presence of the others. The Second Edition of AlvinRencher's Methods of Multivariate Analysis provides studentsof all statistical backgrounds with both the fundamental and moresophisticated skills necessary to master the discipline.
To illustrate multivariate applications, the author providesexamples and exercises based on fifty-nine real data sets from awide variety of scientific fields. Rencher takes a "methods"approach to his subject, with an emphasis on how students andpractitioners can employ multivariate analysis in real-lifesituations. The Second Edition contains revised and updatedchapters from the critically acclaimed First Edition as well asbrand-new chapters on:
* Cluster analysis
* Multidimensional scaling
* Correspondence analysis
* Biplots
Each chapter contains exercises, with corresponding answers andhints in the appendix, providing students the opportunity to testand extend their understanding of the subject. Methods ofMultivariate Analysis provides an authoritative reference forstatistics students as well as for practicing scientists andclinicians.

Autoren/Hrsg.

Rencher, Alvin C.

Inhaltsverzeichnis

Introduction.
Matrix Algebra.
Characterizing and Displaying Multivariate Data.
The Multivariate Normal Distribution.
Tests on One or Two Mean Vectors.
Multivariate Analysis of Variance.
Tests on Covariance Matrices.
Discriminant Analysis: Description of Group Separation.
Classification Analysis: Allocation of Observations toGroups.
Multivariate Regression.
Canonical Correlation.
Principal Component Analysis.
Factor Analysis.
Cluster Analysis.
Graphical Procedures.
Tables.
Answers and Hints to Problems.
Data Sets and SAS Files.
References.
Index.

Über Autor(innen)

ALVIN C. RENCHER, PhD, is Professor of Statistics at Brigham Young University and a Fellow of the American Statistical Association. He is the author of Linear Models in Statistics and Multivariate Statistical Inference and Applications, both available from Wiley.