Rilegato

ISBN 10: 0471418897 ISBN 13: 9780471418894

Casa editrice: Wiley-Interscience, 2002

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.

Recensione:

" a systematic, well–written text there is much practical wisdom in this book that is hard to find elsewhere. It belongs in serious data analysts′ libraries " ( IIE Transactions–Quality and Reliability Engineering, November 2005)

"...extends univariate procedures...to analogous multivariate techniques involving several dependent variables..." (SciTech Book News, Vol. 26, No. 2, June 2002)

"...a practitioner who wants to carry out multivariate techniques in applied work and to interpret the results must have this book..." (Technometrics, Vol. 45, No. 1, February 2003)

"...I have not found a better text for a masters–level class in multivariate methods." (Journal of the American Statistical Association, March 2003)

"This book strikes a nice balance between meeting the needs of statistics majors and students in other fields. The discussion of each multivariate technique is straightforward and quite comprehensive. This textbook is likely to become a useful reference for students in their future work." (Journal of the American Statistical Association)

"In this well–written and interesting book, Rencher has done a great job in presenting intuitive and innovative explanations of some of the otherwise difficult concepts." (CHOICE)

"This book is excellent for an introductory course in multivariate analysis for students with minimal background in mathematics and statistics." (Technometrics)

"Excellent introduction to standard topics in multivariate analysis." (American Mathematical Monthly)

" a systematic, well–written text there is much practical wisdom in this book that is hard to find elsewhere. It belongs in serious data analysts′ libraries " ( IIE Transactions–Quality and Reliability Engineering, November 2005)

"...extends univariate procedures...to analogous multivariate techniques involving several dependent variables..." (SciTech Book News, Vol. 26, No. 2, June 2002)

"...a practitioner who wants to carry out multivariate techniques in applied work and to interpret the results must have this book..." (Technometrics, Vol. 45, No. 1, February 2003)

"...I have not found a better text for a masters–level class in multivariate methods." (Journal of the American Statistical Association, March 2003)

L'autore:

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.