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, 2014/15||10 Credits|
|Lecturer:||Dr Frazer Jarvis||uses MOLE||Timetable||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: MAS205 (Statistics Core); MAS274 (Statistical Reasoning) recommended
No other modules have this module as a prerequisite.
- 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.
- Construction of statistical hypothesis tests: the likelihood ratio method and the union-intersection principle.
- Single and two sample methods: Hotelling's T2 test, practical implementation in R.
- Multisample methods: multivariate analysis of variance and connection with crimcoords, interpretation of R analyses.
- Discriminant analysis: probabilities of misclassification, likelihood rules, linear discriminant analysis in R.
- 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
- 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.
Lectures, problem solving
20 lectures, no tutorials
One formal 2 hour written examination. Format: 3 questions from 4 [75%]. Project [25%].
Multivariate data summary
- basic notation, sample estimates of mean, covariance and variance (1 session)
- scatterplots, augmented plots, Andrews’ plots, special techniques (1 session)
- principal component analysis (2 sessions),
- principal component and crimcoord displays (2 sessions),
- implementation in R (1 session).
- revision of univariate likelihood ratio tests (1 session)
- the likelihood ratio method in multivariate data (2 sessions)
- the union-intersection principle (2 sessions).
- Hotelling’s T2 test (1 session)
- practical implementation in R (1 session).
- multivariate analysis of variance and connection with crimcoords (1 session)
- interpretation of R analyses (1 session).
- probabilities of misclassification (2 sessions)
- likelihood rules (1 session)
- linear discriminant analysis in R. (1 session)
|B||Cox, T.||An introduction to multivariate data analysis||519.535||Blackwells||Amazon|
|B||Everitt||An R and S-PLUS companion to multivariate analysis||519.535||Blackwells||Amazon|
|B||Gnanadesikan||Methods for statistical data analysis of multivariate observations||519.53||Blackwells||Amazon|
|B||Mardia, Kent and Bibby||Multivariate analysis||519.53||Blackwells||Amazon|
|C||Everitt||Applied multivariate data analysis||519.53||Blackwells||Amazon|
|C||Manly||Multivariate statistical methods : a primer||519.535||Blackwells||Amazon|
(A = essential, B = recommended, C = background.)
Most books on reading lists should also be available from the Blackwells shop on Mappin Street.
|Mon||15:00 - 15:50||lecture||Hicks Lecture Theatre 5|
|Wed||12:00 - 12:50||lecture||Arts Tower Lecture Theatre 1|