## MAS465 Multivariate Data Analysis

Note: This is an old module occurrence.

You may wish to visit the module list for information on current teaching.

 Semester 1, 2017/18 10 Credits Lecturer: Dr Frazer Jarvis uses MOLE Reading List Aims Outcomes Teaching Methods Assessment Full Syllabus

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 in standard computer packages.

Prerequisites: MAS223 (Statistical Inference and Modelling)
No other modules have this module as a prerequisite.

## Outline syllabus

• Multivariate data summary: sample estimates of mean, covariance and variance
• Graphical displays: scatterplots, augmented plots, Andrews' plots, special techniques.
• Exploratory analysis and dimensionality reduction: principal component analysis, principal component and crimcoord displays, implementation in R.
• Multidimensional scaling: visualisation of similarity data.
• Linear discriminant analysis: visualisation of grouped data, linear discriminant analysis in R.
• Multivariate normal distribution: basic properties, confidence regions, simple hypothesis tests, statistical discriminant analysis.
• Single and two sample methods: Hotelling's T2 test, practical implementation in R.
• Construction of statistical hypothesis tests: the likelihood ratio method and the union-intersection principle, MANOVA, implementation in R.

## Office hours

As I said in lectures, the MOLE discussion board is the best way to get in contact. But I'll try to be in my office (J12) on Thursday mornings 9.30-11.30 for any questions you may want to ask in person.

## Aims

• To illustrate extensions of univariate statistical methodology to multivariate data.
• To introduce students to some of the statistical methodologies which arise only in multivariate data.
• To introduce students to some of the computational techniques required for multivariate analysis available in standard statistical packages

## Learning outcomes

• have some understanding of techniques of multivariate data summary and graphical display and of the principles of multivariate exploratory data analysis and dimensionality reduction;
• have some understanding of the construction of multivariate likelihood ratio tests and of the union-intersection principle in multivariate testing;
• be able to perform and interpret principal component analysis and linear discriminant analysis using a computer package;
• be able to understand the results of computer based multivariate analyses of one and two sample tests;
• be familiar with facilities offered by computer packages for multivariate analysis.

## Teaching methods

Lectures, problem solving

20 lectures, no tutorials

## Assessment

One formal 2 hour written examination. Format: 3 questions from 4 [75%]. Project [25%].

## Full syllabus

Multivariate data summary

• basic notation, sample estimates of mean, covariance and variance
Graphical displays
• Scatterplots, augmented plots, Andrews plots, special techniques
Exploratory analysis and dimensionality reduction
• principal component analysis
• principal component and crimcoord displays
• Multidimensional scaling
• Linear discriminant analysis
• Cluster analysis and other data mining techniques
Construction of statistical hypothesis tests
• multivariate normal distribution and confidence regions
• Hotelling's T2 test
• statistical discriminant analysis
• practical implementation in R
• the likelihood ratio method in multivariate data
• the union-intersection principle
• MANOVA