CA764

DATA ANALYTICS

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

Outline: 

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.

Books:

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.