Książka Applied Multivariate Analysis Neil H. Timm

Applied Multivariate Analysis

Autor: Neil H. Timm
Język: Angielski
Oprawa: Miękka
Wydawca: Springer, Berlin
Dostępność: Dostępna u dostawcy
Wysyłamy za 5-8 dni
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This book provides a broad overview of the basic theory and methods of applied multivariate analysis...

Informacje o książce

Autor
Język
Angielski
Oprawa
Książka - Miękka
Data wydania
2013
strony
695
EAN
9781441929631
ISBN
1441929630
Enbook ID
01421690
Waga
1293
Wymiary
178 x 235 x 39

Pełny opis

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.This textbook 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. The book includes an overview of vectors, matrices, multivariate distribution theory, and multivariate linear models. Topics discussed include multivariate regression, multivariate analysis of variance for fixed and mixed models, seemingly unrelated regression models and repeated measurement models. While standard procedures for estimating model parameters and testing multivariate hypotheses, as well as simultaneous test procedures, are discussed and illustrated in the text, the text also includes tests of multivariate normality with chi-square and beta plots, tests of multivariate nonadditivity, tests of covariance structure, tests of nonnested hypotheses, and the assessment of model assumptions. Other topics illustrated in the text include discriminant and classification analysis, principal component analysis, canonical correlation analysis, exploratory factor analysis, cluster analysis, multidimension scaling, and structural equation modeling. The text should appeal to practitioners, researchers, and applied statisticians. It may be used in a one-semester course in applied multivariate analysis for practitioners and researchers, or as a two-semester course for majors in applied statistics. Because most data analyzed in the social and behavioral sciences and other disciplines involve many continuous variables, the techniques and examples. SAS programs for this book are available on the Springer website.

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