Course Title: Multivariate Analysis

Part A: Course Overview

Course Title: Multivariate Analysis

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016


City Campus


171H School of Science


Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021,
Sem 2 2022

Course Coordinator: Professor Irene Hudson

Course Coordinator Phone: +61 3 9925 3224

Course Coordinator Email:

Course Coordinator Location: 08.09.28

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

This course builds on the material presented in the following courses:  MATH2199 Probability and Statistics and MATH2155 Statistical Inference.
Alternatively, if you have a good knowledge of univariate statistical Inference, that is univariate estimation procedures and statistical hypotheses testing, you will be able to do this course.

Course Description

The course will cover both extensions of univariate techniques such as estimation, hypothesis testing, and techniques unique to the multivariate setting such as the dimension reduction method of principal components analysis. Group structure in multivariate measurements will be explored with principal component analysis, factor analysis, discriminant analysis and cluster analysis. Skills will be developed with SAS, a leading statistical analysis software package.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. See the WAM information web page for more information.

Objectives/Learning Outcomes/Capability Development

Course Learning Outcomes (CLOs):

On successful completion of this course, you will be able to

  1. Perform exploratory analysis of multivariate data, such as plot multivariate data, calculating descriptive statistics, testing for multivariate normality;
  2. Conduct statistical inference about multivariate means including hypothesis testing, confidence ellipsoid calculation and different types of confidence intervals estimation;
  3. Undertake statistical analyses using appropriate multivariate techniques, which includes principal component, factor analysis, discriminant and clustering analysis;
  4. Analyse multivariate data using the SAS statistical software package.

This course contributes to the development of the following Program Learning Outcomes:

Knowledge and technical competence

  • The ability to use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • The ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • An understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Overview of Learning Activities

There will be a combination of lectorials to cover theoretical concepts and practical lab sessions to apply theory to practice and analytics using the SAS package.

The course will be delivered using lectorials including interactive problem sessions. Pre-recorded lectures will explain theories underlying multivariate techniques, with demonstrations using real applications from varied disciplines. The course material is designed to offer you a balance between theory and applied examples. In addition the problem sessions will provide students opportunities to learn programming for multivariate data analysis using the industry standard package SAS.

All course materials such as lecture notes, lab practice, exercises and assessment can be accessed from Canvas LMS.

Overview of Learning Resources

A list of recommended textbooks for this course is provided on Canvas. All course materials, including lecture notes, computer exercises, practical exercises and assignments will be posted on Canvas LMS.

The statistical package SAS can be accessed from the school computer labs, or students can get access to SAS through the RMIT MyDesktop system anywhere anytime.

Library Subject Guide for Mathematics & Statistics

Overview of Assessment

This course has no hurdle requirements

Assessment Task 1: Problem based quizzes
Weighting: 30%
This assessment task supports CLOs 1, 4

Assessment Task 2: Quiz
Weighting: 30%
This assessment task supports CLOs 1, 2, 3, 4

Assessment Task 3: Project
Weighting: 40%
This assessment supports CLOs 2 & 3