STA347 Multivariate Statistical Analysis (8)

Abstract

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

+ Subject Availability Modes and Location

Session 2
Distance	Wagga Wagga Campus

Continuing students should consult the SAL for current offering details: STA347
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 session	HD/FL	School of Computing and Mathematics

Enrolment restrictions

Prerequisite(s)
STA201 or STA401

Learning Outcomes

Upon successful completion of this subject, students should:

be familiar with notations and be able to perform basic matrix algebra used in multivariate analysis;
be able to analyse multivariate data with summary estimates and correct visualisation;
be able to classify given problems by their appropriate analysis techniques;
be able to apply the chosen technique to the solution of the problem;
be able to report and explain the results of the analysis;
be able to use statistical packages in their data analysis.

Syllabus

The subject will cover the following topics:

Introductory 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.

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The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.