This module will introduce the basics of multivariate statistical analysis to students.  The first few weeks the module deals with classic multivariate topics.  The last few weeks the module presents modern multivariate tools for classification and clustering and dimension reduction.  Throughout the semester the students will also use the lab to learn how to apply the techniques taught in class on large datasets using statistical software. 

The goal of the module is to help the students get a broad knowledge of how to handle multivariate problems.  The module is aimed to students with an OR/Stats degree who will likely encounter multivariate data in their careers as these become the norm in most real life problems.

 Prerequsite Modules: MA2500 Foundations of Probability and Statistics

• Use of multivariate distributions for vectors and matrices in hypothesis testing.
• Fit multivariate linear models using statistical software
• Use classification, clustering and dimension reduction tools to analyze high dimensional datasets 

22 - 50 minute lectures

11 - 50 minute lab lectures

Some handouts will be provided in hard copy or via Learning Central, but students will be expected to take notes of lectures.

Students are also expected to undertake at least 50 hours private study including preparation of worked solutions for tutorial classes.

Analyzing multivariate and high dimensional data.

Modeling using classic and modern multivariate tools.

Transferable Skills:
Applying modern statistical tools to high dimensional data

The in-course element of summative assessment is based on selected homework problems which will allow the student to demonstrate their understanding of the theoretical basis of the methods discussed.  Also, there will be a project where students will be given a real dataset to analyse using computing software.

The major component of summative assessment is the written examination at the end of the module.  This gives students the opportunity to demonstrate their overall achievement of learning outcomes.  It also allows them to give evidence of the higher levels of knowledge and understanding required for above average marks.  The examination paper has a choice of three from four equally weighted questions.

• Random vectors and matrices
• Multivariate Distributions
• Multivariate Estimation and Testing
• Multivariate Regression and Linear models
• Dimension Reduction methods
• Clustering and Classification methods

Anderson, T. W.  (2003) An Introduction to Multivariate Statistical Analysis, Wiley, Hoboken, NJ.

Bilodeau, M. and Brenner, D. (1999) Theory of Multivariate Statistics, Springer-Verlag, NY

Izenman,  A. J. (2008) Modern Multivariate Statistical Techniques. Regression, Classification and Manifold Learning, Springer, NY