Syllabus and Time Table

Unit 1

Multivariate Normal Distribution: Multivariate Normal Distribution Functions, Conditional Distribution and its relation to regression model, Estimation of parameters.

Unit 2

Multiple Linear Regression Model: Standard multiple regression models with emphasison detection of collinearity, outliers, non-normality and autocorrelation, Validation of model assumptions. Multivariate Regression: Assumptions of Multivariate Regression Models, Parameter estimation, Multivariate Analysis of variance and covariance

Unit 3

Discriminant Analysis: Statistical background, linear discriminant function analysis,Estimating linear discriminant functions and their properties. 

Unit 4

Principal Component Analysis: Principal components, Algorithm for conducting principal component analysis, deciding on how many principal components to retain, H-plot. Factor Analysis: Factor analysis model, Extracting common factors, determining numberof factors, Transformation of factor analysis solutions, Factor scores.

Unit 5

Cluster Analysis: Introduction, Types of clustering, Correlations and distances, clustering by partitioning methods, hierarchical clustering, overlapping clustering, K-Means Clustering-Profiling and Interpreting Clusters.