The course is application based. SPSS or SAS package will be used for applications and analysis part. The theory content is worth is 70 % and 30 % is for SPSS or SAS exercises.

Pre-requisites: CA 761, CA 764


Spatial map using metric and non-metric data, Naming and interpreting the dimensions using canonical correlation.

Attribute based perceptual map using factor analysis, Spatial map using preference data through simple Euclidean model.

Cluster Analysis-Similarity measures - clustering Techniques: Hierarchical and partitioning methods, Graphical methods, Assessing cluster solutions.

Canonical Correlation Analysis-Canonical Variates, and Correlations.

Interpreting the Population Canonical Variates, Sample Canonical Variates and sample Canonical correlations, Large Sample Inferences; MANOVA.


1. Richard A. Johnson and Dean W. Wichern, "Applied Multivariate Statistical Analysis", 5th Edition, Pearson Education, 2002.

2. William R. Dillon and Mathew Goldstein, "Multivariate Analysis: Methods and applications", John Wiley and Sons, 1984.