Table of contents:
Overview of multivariate methods --
Examining your data --
Exploratory factor analysis --
Multiple regression analysis --
Multiple discriminant analysis --
Logistic regression: regression with a binary dependent variable --
Conjoint analysis --
Cluster analysis --
Multidimensional scaling --
Analyzing nominal data with correspondence analysis --
Structural equations modeling overview --
Confirmatory factor analysis --
Testing structural equations models --
Manova and glm.

Summary:
For graduate and upper-level undergraduate marketing research courses. 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