Multivariate Statistical Methods
Inference about mean vectors and covariance matrices; multivariate analysis of variance and regression; canonical correlation; discriminant, cluster, principal component, and factor analysis.
STAT
666
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 535 & STAT 624 & STAT 642
 TaughtFall
Course Outcomes: 


STAT 666

At the end of Stat 666 the student will be able to:

Demonstrate Understanding

Demonstrate understanding of multivariate random vectors and their distributions

Basic Principles of Probability

Use basic principles of probability, statistics, and linear algebra to motivate:

  • the comparison of mean vectors
  • multivariate regression and canonical correlation
  • principal component analysis and factor analysis
  • classification and clustering

Produce Analysis

Produce a complete analysis of appropriate multivariate data using SAS and R

"Consulting" Project

Take a multivariate data analysis "consulting" project and

  • Indentify appropriate statistical approaches to address the clients problem
  • Carry out a thorough and meaningful analysis of the data
  • Clearly and effectively defend and communicate their approach and findings to their client in a report