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.
Author(s): Alvin C. Rencher
Series: Wiley series in probability and mathematical statistics
Edition: 2nd ed
Publisher: J. Wiley
Year: 2002
Language: English
Pages: 738
City: New York
Cover......Page 1
Contents......Page 6
Preface......Page 16
Ch1 Introduction......Page 24
Ch2 Matrix Algebra......Page 28
Ch3 Characterizing & Displaying Multivariate Data......Page 66
Ch4 Multivariate Normal Distribution......Page 105
Ch5 Tests on 1 or 2 Mean Vectors......Page 135
Ch6 Multivariate Analysis of Variance......Page 179
Ch7 Tests on Covariance Matrices......Page 271
Ch8 Discriminant Analysis: Description of Group Separation......Page 293
Ch9 Classification Analysis: Allocation of Observations to Groups......Page 322
Ch10 Multivariate Regression......Page 345
Ch11 Canonical Correlation......Page 384
Ch12 Principal Component Analysis......Page 403
Ch13 Factor Analysis......Page 431
Ch14 Cluster Analysis......Page 474
Ch15 Graphical Procedures......Page 527
AppA Tables......Page 572
AppB Answers & Hints to Problems......Page 614
AppC Data Sets & SAS Files......Page 702
References......Page 704
Index......Page 718
Wiley Series in Probability & Statistics......Page 732
