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


General Linear Regression Model, Estimation for β, Error Estimation, Residual Analysis.

Tests of significance - ANOVA, ‘t’ test, Forward, Backward, Sequential, Stepwise, All possible subsets, Dummy Regression, Logistic Regression, Multi-collinearity.

Discriminant Analysis-Two group problem, Variable contribution, Violation of assumptions, Discrete and Logistic Discrimination, The k-group problem, multiple groups, Interpretation of Multiple group Discriminant Analysis solutions.

Principal Component Analysis-Extracting Principal Components, Graphing of Principal Components, Some sampling Distribution results, Component scores, Large sample Inferences, Monitoring Quality with principal Components.

Factor Analysis-Orthogonal Factor Model, Communalities, Factor Solutions and rotation.


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

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