This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.
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Editorial Reviews
Review
From the reviews: "This book is more than an up-to-date textbook on multivariate analysis. It could enable SAS users to take full and informed advantage of the many options offered in the SAS procedures. For non-SAS users, the clear statement of the models should enable them to fit and interpret them with other software." ISI Short Book Reviews, Vol. 23/2, August 2003 "This textbook is another comprehensive work on applied multivariate analysis. Basic theory and methods are reviewed and illustrated by a number of examples and practices. … The author has written a useful textbook combining most of general theory and practice of multivariate data analysis. The book is suitable to familiarize students at graduate level with main concepts and principles of multivariate analysis." (Dr. ir. M. H. J. de Bruijne, Kwantitatieve Methoden, Vol. 70B37, 2003) "This text is on the analysis of structured data … . The author has managed to encapsulate so much in this book by giving a clear statement of each model … . This book is more than an up-to date textbook on multivariate analysis. It could enable SAS users to take full and informed advantage of the many options offered in the SAS procedures. For non-SAS users, the clear statement of the models should enable them to fit and interpret them with other software." (J. M. Juritz, Short Book Reviews, Vol. 23 (2), 2003) "I was extremely pleased to see this book arrive. … For each subject, all important equations and distributional results are very clearly stated. … I found this book exciting, interesting and informative. The exercises are quite well chosen … . In summary, Applied Multivariate Analysis is an excellent book. If you want only one book on multivariate analysis, I would suggest this as a strong candidate. I am extremely glad that I own this book … ." (David E. Booth, Technometrics, Vol. 45 (2), May, 2003) "This textbook provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates theory and practice including both the analysis of formal linear multivariate models and exploratory date analysis techniques. … The techniques and examples discussed in the book should be helpful in the analysis of multivariate data using SAS. All programs and data sets used may be downloaded from a Web site. The book appeals to practitioners, researchers, and applied statisticians." (T. Postelnicu, Zentralblatt MATH, Vol. 1002 (2), 2003)
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"This book is more than an up-to-date textbook on multivariate analysis. It could enable SAS users to take full and informed advantage of the many options offered in the SAS procedures. For non-SAS users, the clear statement of the models should enable them to fit and interpret them with other software." ISI Short Book Reviews, Vol. 23/2, August 2003
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This review is from: Applied Multivariate Analysis (Hardcover)
Timm's textbook tries to involve a lot topics pertaining to multivariate analysis. So it is more comprehensive than Johnson and Wichern's Applied multivariate statistical analysis. Moreover, the book uses SAS program to illustrate some examples, which are available from the website of the textbook. In this way, the content of the book are actually doubled. SAS examples also help students to learn how to use SAS to solve problems.
Some weaknesses includes 1). many typos are found the textbook; 2). the explanation about some foundamental theories is not as clear as that in Jonhson and Wichern's book.
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Inside This Book
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First Sentence:
In this book we present applied multivariate data analysis methods for making inferences regarding the mean and covariance structure of several variables, for modeling relationships among variables, and for exploring data patterns that may exist in one or more dimensions of the data. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
protein consumption data, conditions given parallelism, given sphericity, audiovisual variables, group location problem, multivariate lack, necessary condition for model identification, partial principal components, covariance loadings, approximate simultaneous confidence intervals, empirical critical values, distances drs, univariate mixed model, multivariate criteria, largest root criterion, nonhierarchical clustering methods, finite intersection tests, multivariate mixed models, proc glm, compound symmetry structure, random coefficient regression model, simultaneous confidence sets, nonorthogonal designs, beta plot, duplication matrix Key Phrases - Capitalized Phrases (CAPs): (learn more)
University of Pittsburgh, Performance Assessment, Full Gamma, Discriminant Structure Vectors, Monte Carlo, Project Talent, Syx Sxx Sxy, Equal Weight, Following Fujikoshi, Peabody Picture Vocabulary Test, Summary of Principal-Component Analysis Using, Two-Sample Case New!
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Front Cover | Table of Contents | First Pages | Index | Surprise Me!
In this book we present applied multivariate data analysis methods for making inferences regarding the mean and covariance structure of several variables, for modeling relationships among variables, and for exploring data patterns that may exist in one or more dimensions of the data. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
protein consumption data, conditions given parallelism, given sphericity, audiovisual variables, group location problem, multivariate lack, necessary condition for model identification, partial principal components, covariance loadings, approximate simultaneous confidence intervals, empirical critical values, distances drs, univariate mixed model, multivariate criteria, largest root criterion, nonhierarchical clustering methods, finite intersection tests, multivariate mixed models, proc glm, compound symmetry structure, random coefficient regression model, simultaneous confidence sets, nonorthogonal designs, beta plot, duplication matrix Key Phrases - Capitalized Phrases (CAPs): (learn more)
University of Pittsburgh, Performance Assessment, Full Gamma, Discriminant Structure Vectors, Monte Carlo, Project Talent, Syx Sxx Sxy, Equal Weight, Following Fujikoshi, Peabody Picture Vocabulary Test, Summary of Principal-Component Analysis Using, Two-Sample Case New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
This book cites 63
books:
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37
books
cite this book:
See all 37 books citing this book
- Multivariate Analysis: 1st: International Symposium Proceedings by Paruchuri R. Krishnaiah on 6 pages
- Advanced Linear Modeling by Ronald Christensen in Front Matter, and page
- Design and Analysis of Experiments (Springer Texts in Statistics) by Angela M. Dean in Front Matter, and Back Matter
- Discrete Mathematics by Wiley in Back Matter (1), and Back Matter (2)
- Theory of Multivariate Statistics (Springer Texts in Statistics) by Martin Bilodeau in Front Matter, and page
See all 63 books this book cites
- Computer-Aided Multivariate Analysis, Fourth Edition (Chapman & Hall/CRC Texts in Statistical Science) by Abdelmonem Afifi on 4 pages
- A Handbook of Statistical Analyses Using R by Brian S. Everitt on page 72, and Back Matter
- Nonlinear Time Series Analysis of Business Cycles, Volume 276 (Contributions to Economic Analysis) by Costas Milas on page 149
- Modeling Longitudinal Data (Springer Texts in Statistics) by Robert E. Weiss in Back Matter
- Monte Carlo Statistical Methods (Springer Texts in Statistics) by Christian Robert in Back Matter
See all 37 books citing this book
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