Sharma S. Applied Multivariate Techniques

1st edition. — Wiley, 1995. — 512 p.The author uses a novel approach to provide conceptual discussions of the various techniques by applying geometry to present the concepts. A small data set, along with hand calculations, illustrates the main point of the method and is then analyzed using standard statistical packages such as SPSS and SAS. An annotated interpretation of the output is included and contains the diverse assumptions made by the technique and their effect on the results. Most of the chapters contain appendices which offer technical details and can be used by those well-versed in matrix algebra. The accompanying disk includes data sets for end-of-chapter exercises featured in the text and many that are not.Geometric concepts of data manipulation
Fundamentals of data manipulation
Principal components analysis
Factor analysis
Confirmatory factor analysis
Cluster analysis
Two-group discriminant analysis
Multiple-group discriminant analysis
Logistic regression
Multivariate analysis of variance
Assumptions
Canonical correlation
Covariance structure models