IL4201 Multi-Variate Data Analysis Syllabus:

IL4201 Multi-Variate Data Analysis Syllabus – Anna University PG Syllabus Regulation 2021

OBJECTIVES:

 Understanding the basic overview on multi variate data analysis
 Predicting the values of one or more variables on the basis of observations on the other variables.
 Formulating the specific statistical hypotheses, in terms of the parameters of multi variate populations
 Data reduction or structural simplification as simply as possible without sacrificing valuable information and will make interpretation easier.
 Sorting and Grouping “similar” objects or variables are created, based upon measured characteristics.

UNIT I REGRESSION

Simple Regression and Correlation – Estimation using the regression line, Correlation analysis, Multiple regression and Correlation analysis – Finding the Multiple Regression equation, Modelling techniques, Making inferences about the population parameters.

UNIT II MULTIVARIATE METHODS

An overview of Multivariate methods, Multivariate Normal distribution, Eigen values and Eigen vectors.

UNIT III FACTOR ANALYSIS

Principal Component Analysis – Objectives, Estimation of principal components, Testing for independence of variables, Factor analysis model – Factor analysis equations and solution – Exploratory Factor analysis – Confirmatory Factor analysis.

UNIT IV DISCRIMINANT ANALYSIS

Discriminant analysis – Discrimination for two multivariate normal Populations – Discriminant functions – Structured Equation Modelling (SEM).

UNIT V CLUSTER ANALYSIS

Cluster analysis – Clustering methods, Multivariate analysis of Variance.

TOTAL : 45 PERIODS

OUTCOMES:

CO1: To understand the basic overview on multi variate data analysis
CO2: Predict the values of one or more variables on the basis of observations on the other variables.
CO3: Formulate the specific statistical hypotheses, in terms of the parameters of multi variate populations
CO4: Data reduction or structural simplification as simply as possible without sacrificing valuable information and will make interpretation easier.
CO5: Sorting and Grouping “similar” objects or variables are created, based upon measured characteristics.

REFERENCES:

1. Dallas E Johnson, Applied Multivariate methods for data analysis, Duxbury Press(2010).
2. Joseph F. Hair, Jr. William C. Black Barry J. Babin, Rolph E. Anderson, Multivariate Data Analysis, Pearson Edition, (2010).
3. Richard I Levin, Statistics for Management, PHI (2011).