STA448 Multivariate Statistical Analysis (8)


This subject introduces students to multivariate statistical modelling techniques through an applied approach to data analysis. The emphasis is on demonstration of techniques and their applicability via investigations of the various methodologies. The use of real life data and software packages is also emphasised in this subject to illustrate the power and limitations of multivariate methods.

+ Subject Availability Modes and Location

Session 2
DistanceWagga Wagga Campus
Continuing students should consult the SAL for current offering details: STA448
Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Computing and Mathematics

Assumed Knowledge


Enrolment restrictions

Not available to students who have completed STA347
Incompatible subject(s)Related subject(s)
STA347 STA347 Covrers Similar Content

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to use a variety of matrix notations and perform matrix algebra used in multivariate analysis;
  • be able to analyse multivariate data with summary estimates and correct visualisation;
  • be able to classify given complex problems, arising from a variety of professional or research contexts, by checking assumptions to select the appropriate analysis techniques;
  • be able to apply the chosen technique to solve the complex problem;
  • be able to report and explain the results of the analysis to a variety of audiences, including those with or without statistical backgrounds;
  • be able to use statistical packages in analysis of real data arising from a variety of professional or research contexts.


The subject will cover the following topics:
  • Introductory matrix notations and matrix algebra.
  • Multivariate data: summary statistics and graphical visualisation.
  • The Multivariate Normal Distribution.
  • Principal Components analysis.
  • Discriminant analysis.
  • Multivariate analysis of variance (MANOVA).
  • Canonical correlation analysis.


The information contained in the 2017 CSU Handbook was accurate at the date of publication: 12 August 2016. The University reserves the right to vary the information at any time without notice.