Multivariate Statistical Analysis

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Abstract

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 … For more content click the Read More button below.

Syllabus

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.

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; and

be able to use statistical packages in analysis of real data arising from a variety of professional or research contexts.

Assumed knowledge

STA201 or STA401 or STA501

Enrolment restrictions

Not available to students who have completed STA347

Incompatible