Multivariate Data Analysis / Edition 7

Multivariate Data Analysis / Edition 7

by Joseph F. Hair Jr, William C. Black, Barry J. Babin, Rolph E. Anderson
     
 

KEY BENEFIT: For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair, et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students howSee more details below

Overview

KEY BENEFIT: For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair, et. al provides an applications-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. In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.
Preparing For a MV Analysis; Dependence Techniques; Interdependence Techniques; Moving Beyond the Basic Techniques
MARKET: Statistics and statistical research can provide managers with invaluable data. This textbook teaches them the different kinds of analysis that can be done and how to apply the techniques in the workplace.

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Product Details

ISBN-13:
9780138132637
Publisher:
Prentice Hall
Publication date:
03/05/2009
Series:
Pearson Custom Business Resources Series
Edition description:
New Edition
Pages:
816
Sales rank:
399,214
Product dimensions:
8.20(w) x 10.00(h) x 1.60(d)

Related Subjects

Table of Contents

I Introduction
1 Introduction

II Preparing For a MV Analysis
2 Examining Your Data
3 Factor Analysis

III Dependence Techniques
4 Multiple Regression Analysis
5 Multiple Discriminate Analysis and Logistic Regression
6 Multivariate Analysis of Variance
7 Conjoint Analysis

IV Interdependence Techniques
8 Cluster Analysis
9 Multidimensional Scaling and Correspondence Analysis

V Moving Beyond the Basic Techniques
10 Structural Equation Modeling: Overview
10a Appendix – SEM
11 CFA: Confirmatory Factor Analysis
11a Appendix – CFA
12 SEM: Testing A Structural Model
12a Appendix – SEM

APPENDIX
A Basic Stats

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