Multivariate Data Analysis
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ISBN13:9781473756540
出版社:Cengage Learning
作者:Joseph F. Hair Jr. ; William C. Black; Barry J. Babin; Rolph E. Anderson
出版日:2019/04/01
裝訂/頁數:平裝/813頁
規格:26cm*19.5cm*2.5cm (高/寬/厚)
重量:1380克
版次:8
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The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today’s world of analytics. Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.
作者簡介
作者:Joseph F. Hair, Jr.
現職:University of South Alabama
作者:William C. Black
現職:Louisiana State University
作者:Barry J. Babin
現職:Louisiana Tech University
作者:Rolph E. Anderson
現職:Drexel University
現職:University of South Alabama
作者:William C. Black
現職:Louisiana State University
作者:Barry J. Babin
現職:Louisiana Tech University
作者:Rolph E. Anderson
現職:Drexel University
目次
Ch 1 Overview of Multivariate Methods
SECTION I: PREPARING FOR MULTIVARIATE ANALYSIS
Ch 2 Examining Your Data
SECTION II: INTERDEPENDENCE TECHNIQUES
Ch 3 Exploratory Factor Analysis
Ch 4 Cluster Analysis
SECTION III: DEPENDENCE TECHNIQUES-METRIC OUTCOMES
Ch 5 Multiple Regression Analysis
Ch 6 MANOVA: Extending ANOVA
SECTION IV: DEPENDENCE TECHNIQUES-NON-METRIC OUTCOMES
Ch 7 Multiple Discriminant Analysis
Ch 8 Logistic Regression: Regression with a Binary Dependent Variable
SECTION V: MOVING BEYOND THE BASICS
Ch 9 Structural Equation Modeling: An Introduction
Ch10 SEM: Confirmatory Factor Analysis
Ch11 Testing Structural Equation Models
Ch12 Advanced SEM Topics
Ch13 Partial Least Squares Structural Equation Modeling (PLS-SEM)
SECTION I: PREPARING FOR MULTIVARIATE ANALYSIS
Ch 2 Examining Your Data
SECTION II: INTERDEPENDENCE TECHNIQUES
Ch 3 Exploratory Factor Analysis
Ch 4 Cluster Analysis
SECTION III: DEPENDENCE TECHNIQUES-METRIC OUTCOMES
Ch 5 Multiple Regression Analysis
Ch 6 MANOVA: Extending ANOVA
SECTION IV: DEPENDENCE TECHNIQUES-NON-METRIC OUTCOMES
Ch 7 Multiple Discriminant Analysis
Ch 8 Logistic Regression: Regression with a Binary Dependent Variable
SECTION V: MOVING BEYOND THE BASICS
Ch 9 Structural Equation Modeling: An Introduction
Ch10 SEM: Confirmatory Factor Analysis
Ch11 Testing Structural Equation Models
Ch12 Advanced SEM Topics
Ch13 Partial Least Squares Structural Equation Modeling (PLS-SEM)
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