Course

Unit I

The organization of Data. Descriptive Statistics. Graphical Techniques. Distance. The Multivariate Normal Distribution-Introduction. The Multivariate Normal Density and its Properties. The sampling Distribution of and S.

Unit II

Classification for two populations, Classification with two Multivariate Normal

populations, Fisher’s Discriminant Functions for discriminating several population.

Unit III

Principal Components Analysis, Dimensionality reduction, Factor Analysis- Factor

Loadings using Principal Component Analysis.

Unit IV

Simple Linear Regression- Properties, Least Squares Estimation of parameters,

Hypothesis Tests in Simple Linear Regression, Test for Significance of Regression using ANOVA. Coefficient of determination.

Unit V

Multiple Linear Regression: Estimation of model parameters. Polynomial Regression Models. Test for Significance of Regression using ANOVA. Coefficient of determination.

Course Outcomes:

CO1: To understand the basics of Multivariate Data and Multivariate Normal Distributions.

CO2: To apply Multivariate Techniques for Classification of Distributions

CO3: To understand the concept of PCA and Factor Analysis

CO4: To gain knowledge on Simple Linear Regression, Estimation and testing of Model

Parameters

CO5: To gain knowledge on Multiple Linear and Polynomial Regression and Estimation of

Model Parameters and testing the significance of regression.

CO-PO Mapping:

Text Books/ Reference Books:

1. Richard A. Johnson, Dean W. (2007). Wichern Applied Multivariate Statistical Analysis Sixth Edition. Pearson Prentie Hall.
2. Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers and Keying Ye. Probability and Statistics for Engineers and Scientists, Eighth Edition, Pearson Education Asia, 2007.
3. Anderson, T. W. (2009): An Introduction to Multivariate Statistical Analysis. Third Edition Wiley.
4. Douglas C. Montgomery and Elizabeth A. Peck and G. Geoffrey Vining 2012. Introduction to Linear Regression Analysis”, Fifth Edition, John Wiley& Sons, Inc