Applied Multivariate Analysis

Neil H. Timm - Collection Springer Series In Statistics

694 pages, parution le 25/07/2002

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Résumé

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.

Sommaire

Introduction.
Vector and Matrix.
Multivariate Distribution and the Linear Model.
Multivariate Regression Models.
Seemingly Unrelated Regression Models
Multivariate Random and Mixed Models.
Discriminant and Classification Analysis.
Principal Component, Canonical Correlation, and Exploratory Factor Analysis.
Cluster Analysis and Multidimensional Scalling.
Structural Equations Models.

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Neil H. Timm
Collection	Springer Series In Statistics
Parution	25/07/2002
Nb. de pages	694
Format	18 x 24
Couverture	Relié
Poids	1380g
Intérieur	Noir et Blanc
EAN13	9780387953472
ISBN13	978-0-387-95347-2