Course Details

Multivariate Analysis


Course ID : QI-BIO-203
Date : For May 2017 - Sept 2017 classes, in NJ, USA/Bangalore, India (depending on no. of students): By Arrangement with Instructor(s)
Duration : 5 days intensive In-Class Training; 2 days Instructor in Classroom to assist students complete assignments
Location : New Jersey, USA and Bangalore, India (depending on no. of students)
Dates: Class Lecture : By Arrangement with Instructor(s)
For Registration/Pricing, call: : Please call our office in New Jersey at 609-454-5635 or email us at info_india@quantument.com for pricing/registration


The main objective of this course is on the application of multivariate statistical methodologies to research data. The topics include multivariate normal distribution; its properties and inference; multivariate analysis of variance (MANOVA), analysis of covariance (MANCOVA) and multivariate regression; multivariate models for repeated measures analysis; principal component analysis, factor analysis, discriminant analysis, cluster analysis and structural equation modeling.

This is an advanced course in multivariate statistical analysis methodology with applications. The choice of statistical software is Statistical Analysis Software (SAS). There will be an extensive use of SAS’s Interactive Matrix Language (IML) procedure as well as procedures such as PRINCOMP, FACTOR, CALIS, DISCRIM in this course.


  • Completion of Advanced Course in Biostatistics.
  • Knowledge of SAS to create and manage data sets as well as experience using Generalized Linear Model procedures in SAS.

Learning Outcomes

After the completion of the course, participants will be able to
  • Have a solid understanding of the statistical methodologies that are appropriate to analyze data that are multivariate in nature
  • Use appropriate procedures in the SAS system to conduct methodologies outlined above and interpret the analytical results.

Topics of Study

The course focuses on the following statistical methodologies with an introduction to matrix algebra:

  • Introduction to Matrices and Linear Algebra
  • Introduction to Multivariate Distribution with Examples
  • Multivariate Analysis of Variance (MANOVA)
  • Multivariate Linear Models
  • Principal Component Analysis
  • Factor Analysis
  • Discriminant Analysis
  • Cluster Analysis
  • Structural Equation Modeling