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724 Pages
26.5 x 18.7 x 4.7
Hardcover
$219.00
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Industry Reviews
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)
| Preface | |
| Acknowledgments | |
| List of Tables | |
| List of Figures | |
| Introduction | p. 1 |
| Vectors and Matrices | p. 7 |
| Introduction | p. 7 |
| Vectors, Vector Spaces, and Vector Subspaces | p. 7 |
| Bases, Vector Norms, and the Algebra of Vector Spaces | p. 12 |
| Basic Matrix Operations | p. 25 |
| Rank, Inverse, and Determinant | p. 41 |
| Systems of Equations, Transformations, and Quadratic Forms | p. 55 |
| Limits and Asymptotics | p. 76 |
| Multivariate Distributions and the Linear Model | p. 79 |
| Introduction | p. 79 |
| Random Vectors and Matrices | p. 79 |
| The Multivariate Normal (MVN) Distribution | p. 84 |
| The Chi-Square and Wishart Distributions | p. 93 |
| Other Multivariate Distributions | p. 99 |
| The General Linear Model | p. 106 |
| Evaluating Normality | p. 118 |
| Tests of Covariance Matrices | p. 133 |
| Tests of Location | p. 149 |
| Univariate Profile Analysis | p. 181 |
| Power Calculations | p. 182 |
| Multivariate Regression Models | p. 185 |
| Introduction | p. 185 |
| Multivariate Regression | p. 186 |
| Multivariate Regression Example | p. 212 |
| One-Way MANOVA and MANCOVA | p. 218 |
| One-Way MANOVA/MANCOVA Examples | p. 234 |
| MANOVA/MANCOVA with Unequal [Sigma][subscript i] or Nonnormal Data | p. 245 |
| One-Way MANOVA with Unequal [Sigma][subscript i] Example | p. 246 |
| Two-Way MANOVA/MANCOVA | p. 246 |
| Two-Way MANOVA/MANCOVA Example | p. 257 |
| Nonorthogonal Two-Way MANOVA Designs | p. 264 |
| Unbalance, Nonorthogonal Designs Example | p. 270 |
| Higher Ordered Fixed Effect, Nested and Other Designs | p. 273 |
| Complex Design Examples | p. 276 |
| Repeated Measurement Designs | p. 282 |
| Repeated Measurements and Extended Linear Hypotheses Example | p. 294 |
| Robustness and Power Analysis for MR Models | p. 301 |
| Power Calculations - Power.sas | p. 304 |
| Testing for Mean Differences with Unequal Covariance Matrices | p. 307 |
| Seemingly Unrelated Regression Models | p. 311 |
| Introduction | p. 311 |
| The SUR Model | p. 312 |
| Seeming Unrelated Regression Example | p. 316 |
| The CGMANOVA Model | p. 318 |
| CGMANOVA Example | p. 319 |
| The GMANOVA Model | p. 320 |
| GMANOVA Example | p. 327 |
| Tests of Nonadditivity | p. 333 |
| Testing for Nonadditivity Example | p. 335 |
| Lack of Fit Test | p. 337 |
| Sum of Profile Designs | p. 338 |
| The Multivariate SUR (MSUR) Model | p. 339 |
| Sum of Profile Example | p. 341 |
| Testing Model Specification in SUR Models | p. 344 |
| Miscellanea | p. 348 |
| Multivariate Random and Mixed Models | p. 351 |
| Introduction | p. 351 |
| Random Coefficient Regression Models | p. 352 |
| Univariate General Linear Mixed Models | p. 357 |
| Mixed Model Examples | p. 369 |
| Mixed Multivariate Models | p. 385 |
| Balanced Mixed Multivariate Models Examples | p. 394 |
| Double Multivariate Model (DMM) | p. 400 |
| Double Multivariate Model Examples | p. 403 |
| Multivariate Hierarchical Linear Models | p. 415 |
| Tests of Means with Unequal Covariance Matrices | p. 417 |
| Discriminant and Classification Analysis | p. 419 |
| Introduction | p. 419 |
| Two Group Discrimination and Classification | p. 420 |
| Two Group Discriminant Analysis Example | p. 429 |
| Multiple Group Discrimination and Classification | p. 434 |
| Multiple Group Discriminant Analysis Example | p. 440 |
| Principal Component, Canonical Correlation, and Exploratory Factor Analysis | p. 445 |
| Introduction | p. 445 |
| Principal Component Analysis | p. 445 |
| Principal Component Analysis Examples | p. 460 |
| Statistical Tests in Principal Component Analysis | p. 468 |
| Regression on Principal Components | p. 474 |
| Multivariate Regression on Principal Components Example | p. 476 |
| Canonical Correlation Analysis | p. 477 |
| Canonical Correlation Analysis Examples | p. 492 |
| Exploratory Factor Analysis | p. 496 |
| Exploratory Factor Analysis Examples | p. 511 |
| Cluster Analysis and Multidimensional Scaling | p. 515 |
| Introduction | p. 515 |
| Proximity Measures | p. 516 |
| Cluster Analysis | p. 522 |
| Cluster Analysis Examples | p. 533 |
| Multidimensional Scaling | p. 541 |
| Multidimensional Scaling Examples | p. 548 |
| Structural Equation Models | p. 557 |
| Introduction | p. 557 |
| Path Diagrams, Basic Notation, and the General Approach | p. 558 |
| Confirmatory Factor Analysis | p. 567 |
| Confirmatory Factor Analysis Examples | p. 575 |
| Path Analysis | p. 580 |
| Path Analysis Examples | p. 586 |
| Structural Equations with Manifest and Latent Variables | p. 594 |
| Structural Equations with Manifest and Latent Variables Example | p. 595 |
| Longitudinal Analysis with Latent Variables | p. 600 |
| Exogeniety in Structural Equation Models | p. 604 |
| Appendix | p. 609 |
| References | p. 625 |
| Author Index | p. 667 |
| Subject Index | p. 675 |
| Table of Contents provided by Blackwell. All Rights Reserved. |
ISBN: 9780387953472
ISBN-10: 0387953477
Series: Springer Texts in Statistics
Published: 19th June 2002
Format: Hardcover
Language: English
Number of Pages: 724
Audience: Professional and Scholarly
Publisher: Springer Nature B.V.
Country of Publication: GB
Dimensions (cm): 26.5 x 18.7 x 4.7
Weight (kg): 1.37
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