Course Title: Multivariate Analysis Techniques

Part A: Course Overview

Course Title: Multivariate Analysis Techniques

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 1 2014,
Sem 1 2016


City Campus


171H School of Science


Sem 2 2018,
Sem 2 2019,
Sem 1 2020,
Sem 2 2020,
Sem 2 2021,
Sem 2 2022,
Sem 2 2023

Course Coordinator: Professor Irene Hudson

Course Coordinator Phone: +61 3 9925 3224

Course Coordinator Email:

Course Coordinator Location: 015.03.019

Course Coordinator Availability: by appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

This course builds on a knowledge of matrix algebra and univariate statistical inference (probability distribution, estimation procedures and statistical hypotheses testing).

Course Description

Multivariate analysis skills have been commonly recognised as part of the key requisites for analytics analysts. The complexity of most phenomena in the real world requires an investigator to collect and analyse observations on many different variables instead of a single variable. The desire for statistical techniques to elicit information from multivariate dimensional data thus becomes essential and crucial for data analysts.

The objective of the course is to introduce several useful multivariate techniques, making strong use of illustrative examples and matrix mathematics. The course will start with the extensions of univariate techniques to multivariate frameworks, such as the multivariate normal distribution, confidence ellipse estimation, hypothesis testing, simultaneous confidence intervals and Bonferroni confidence intervals.  The course will also cover the techniques unique to the multivariate setting such as principal component analysis, factor analysis, discrimination, classification and clustering analysis.

Skills will be developed with SAS, a leading statistical analysis software package used in industry.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical 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 relationship between the purpose of a model and the appropriate level of complexity and accuracy.


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Course Learning Outcomes (CLOs): 

On completion of this course you should 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.

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 Tasks:

Assessment Task 1: Problem Based Quizes
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