CIE351 Multivariate Data Analysis Syllabus:
CIE351 Multivariate Data Analysis Syllabus – Anna University Regulation 2021
COURSE OBJECTIVES:
• To impart knowledge on the Regression
• To understand the concepts of multivariate method
• To apply the knowledge of factor analysis
• To apply the knowledge of discriminant analysis
• To apply the knowledge of cluster analysis
UNIT I MULTIVARIATE METHODS
An overview of Multivariate methods, Multivariate Normal distribution, Eigen values and Eigen vectors.
UNIT II 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 III FACTOR ANALYSIS
Principal Component Analysis – COURSE OBJECTIVES, Estimation of principal components, Testing for the 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
COURSE OUTCOMES:
CO1: Can apply the multivariate, analysis techniques for statistical analysis
CO2: Can apply the regression, analysis techniques for statistical analysis
CO3: Can apply the factor, analysis techniques for statistical analysis
CO4: Can apply the discriminent analysis techniques for statistical analysis
CO5: Can apply the cluster analysis techniques for statistical analysis
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).