To provide an appreciation of the types of problems and questions which arise with multivariate data; to give a good understanding of the application of multivariate techniques for the graphical exploration and analysis of multivariate data.

15 lectures

5 tutorials

10 hours practical sessions

STATS4046 Multivariate Methods

90-minute, end-of-course examination (85%)

Coursework (15%)

To provide an appreciation of the types of problems and questions which arise with multivariate data;

to provide a good understanding of the application of classical multivariate techniques for: the graphical exploration of multivariate data, the reduction of dimensionality of multivariate data and analysis in unsupervised and supervised settings.

By the end of this course students will be able to:

■ display multivariate data in a variety of graphical ways and interpret such displays;

■ apply and interpret methods of dimension reduction including principal component analysis, multidimensional scaling, the biplot, factor analysis, canonical variates;

■ apply and interpret classical methods for cluster analysis and discrimination;

■ use formal criteria for model selection in prediction and model fitting;

■ interpret the output of R procedures for multivariate statistics;

■ read further into one topic related to the course and use these concepts to solve a real-world problem.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.