Multivariate Data Analysis

Course Objective

To provide a strong foundation in multivariate techniques for data analysis and modeling. It covers a thorough discussion of the widely used multivariate techniques and their demonstration using industry based data sets.

Learning Outcomes

Upon completion of this course, students will be able to

Extract useful information from large and complex data sets

Recognize patterns and trends in the data bases and model them.

Use appropriate analytical tools from the set of analysis of variance and covariance, regression analysis, discriminant analysis, logistic regression, factor analysis, cluster analysis, multidimensional scaling, correspondence analysis, conjoint analysis, canonical correlation and structural equation modeling in order to extract maximum information from the data sets.

Use the most powerful and sophisticated routines in R for multivariate data analysis.

Detailed Syllabus

  1. Introduction to Multivariate Data Analysis
  2. Multiple regression and correlation
  3. Multiple discriminant analysis and logistic regression
  4. Canonical correlation analysis
  5. Multivariate analysis of variance and covariance
  6. Principal components and common factor analysis
  7. Cluster analysis
  8. Conjoint analysis
  9. Multidimensional scaling
  10. Correspondence analysis
  11. Structural equation modeling and confirmatory factor analysis
  12. Partial least squares