MAS465   Multivariate Data Analysis   (10 credits)

Description

The analysis of multivariate data requires the extension of standard univariate statistical models and methods but also introduces new problems. Initial attention is given to Data Mining techniques such as summarising and displaying high dimensional data and to ways of reducing multivariate problems to more manageable univariate ones. This is followed by routine generalisations of standard distributions and statistical tests before consideration of new strategies for constructing hypothesis tests. Finally, problems specific to multivariate data such as discrimination and classification (use in medical diagnosis problems for example) are studied. Most of these methods can be implemented using statistical software.

Reading List