MULTIVARIATE DATA ANALYSIS - Semantic Scholar
SEVENTH EDITION MULTIVARIATE data ANALYSIS i .-*.'. . ' -4 A Global Perspective Joseph F. Hair, Jr. Kennesaw State University William C. Black Louisiana State University Barry J. Babin University of Southern Mississippi Rolph E. Anderson Drexel University Upper Saddle River Boston Columbus San Francisco New York Indianapolis London Toronto Sydney Singapore Tokyo Montreal Dubai Madrid Hong Kong Mexico City Munich Paris Amsterdam Cape Town CONTENTS Preface xxv About the Authors xxvii Chapter 1 Introduction: Methods and Model Building 1 What Is MULTIVARIATE ANALYSIS ? 3 MULTIVARIATE ANALYSIS in Statistical Terms 4 Some Basic Concepts of MULTIVARIATE ANALYSIS 4 The Variate 4 Measurement Scales 5 Measurement Error and MULTIVARIATE Measurement 7 Statistical Significance Versus Statistical Power 8 Types of Statistical Error and Statistical Power 9 Impacts on Statistical Power 9 Using Power with MULTIVARIATE Techniques 11 A Classification of MULTIVARIATE Techniques 11 Dependence Techniques 14 Interdependence Techniques
Logistic Regression: Regression with a Binary Dependent Variable 413 Representation of the Binary Dependent Variable 414 Sample Size 415 Estimating the Logistic Regression Model 416 Assessing the Goodness-of-Fit of the Estimation Model 419 Testing for Significance of the Coefficients 421 Interpreting the Coefficients 422
Download MULTIVARIATE DATA ANALYSIS - Semantic Scholar
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: