Overview of multivariate methods -- pt. I. Preparing to apply multivariate analysis. Examining your data ; Exploratory factor analysis -- pt. II. Dependence techniques. Multiple regression analysis ; Multiple discriminant analysis ; Logistic regression : regression with a binary dependent variable ; MANOVA and GLM ; Conjoint analysis -- pt. III. Interdependent techniques. Cluster analysis ; Multidimensional scaling ; Analyzing nominal data with correspondence analysis -- pt. IV. Moving beyond the basic techniques. Structural equations modeling overview ; Confirmatory factor analysis ; Testing structural equations models ; Advanced SEM topics and PLS.
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Text of Note
"Multivariate Data Analysis is an applications-oriented introduction to multivariate analysis for the non-statistician. The seventh edition incorporates pivotal advances in technology that will assist students in gaining a firm understanding of statistical and managerial principles so as to develop a "comfort zone" not only for the statistical, but also the practical issues involved."--Jacket.