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

Course Coordinator: Dr Alice Johnstone

Course Coordinator Phone: +61 3 9925 2683

Course Coordinator Email:

Course Coordinator Location: 08.09.78

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. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.)

The WAM web page link:;ID=eyj5c0mo77631

Objectives/Learning Outcomes/Capability Development

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

  • Perform exploratory analysis of multivariate data collected within your field of specialisation;
  • Test for multivariate normality of the data;
  • Apply multivariate statistical methods via hypothesis testing, point estimation and confidence interval estimation;
  • Perform data reduction using principal component analysis;
  • Apply multivariate techniques such as factor analysis and cluster analysis to study the population structure;
  • Use SAS, statistical software packages to analyse multivariate data.

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

You will attend three hours of lectures per week  and a one hour computer laboratory session per week. 
If you are experiencing difficulty in understanding the lecture material you may seek free help from the lecturer during the advertised consulting times.

Overview of Learning Resources

There will be a prescribed textbook and a list of recommended textbooks for this course. In addition you will be given lecture notes and assignments.

Overview of Assessment


This course has no hurdle requirements

Assessment Task 1: Assignments

Weighting 25%

This assessment task supports CLOs 1, 2&3


Assessment Task 2: Mid-Semester Test

Weighting 25%

This assessment task supports CLOs 1, 2&3


Assessment 3: Two-hour final examination (partial open book)

Weighting 50% 

This assessment supports CLOs 2&3