MATH48062 - 2008/2009
- Title: Multivariate Statistical Methods
- Unit code: MATH48062
- Credits: 15
- This course unit may not be taken as well as MATH38062.
- Prerequisites: MATH20701, MATH20802 Statistical Methods.
- Co-requisite units: None
- School responsible: Mathematics
- Members of staff responsible:
To familiarise students with the ideas and methodology of certain multivariate methods together with their application in data analysis using the S-Plus statistical computing package.
Brief Description of the unit
In practice most sets of data are multivariate in that they consist of observations on several different variables for each of a number of individuals or objects. Indeed, such data sets arise in many areas of science, the social sciences and medicine and techniques for their analysis form an important area of statistics. This course unit introduces a number of techniques, some of which are generalisation of univariate methods, while others are completely new (e.g. principal component analysis). It contains a number of additional topics to those in the third level course unit.
On successful completion of this course unit students will
- be familiar with multivariate random vectors and their probability distributions;
- have acquired skills in dimensionality reduction techniques, graphical presentations, inferential methods based on the multivariate Normal distribution as an underlying model and in methods of data classification;
- be able to interpret output from the computer software package R.
Future topics requiring this course unit
This course unit forms a useful background to other statistics course units.
- Introductory ideas and basic concepts, including simple graphical techniques.
- Cluster Analysis, multidimensional scaling.
- Dimensionality reduction: Techniques based on the singular value decomposition of a data matrix - principal component analysis, biplots.
- Canonical correlation analysis.
- The Multivariate Normal (MVN) distribution: Definition. Properties. Conditional distributions. The Wishart and Hotelling T-squared distributions. Maximum likelihood estimation of the mean vector and covariance matrix.
- Hypothesis testing and confidence intervals (1 and 2 sample procedures).
- Multivariate Analysis of Variance.
- Chatfield, C. and Collins, A. J., An Introduction to Multivariate Analysis, Chapman & Hall 1983.
- Krzanowski, W. J., Principles of Multivariate Analysis: A User's Perspective, Oxford University Press 1990.
- Johnson, R. A. and Wichern, D. W., Applied Multivariate Statistical Analysis 3rd edition, Prentice Hall 1992.
- Everitt, B.S. and Dunn, G. Applied Multivariate Data Analysis, (2nd edition), Arnold 2001,
- Cox, T.F. An introduction to Multivariate Data Analysis, Hodder Arnold, 2005.
Teaching and learning methods
Three lectures and one examples class each week. In addition students should expect to spend at least six hours each week on private study for this course unit.
- Coursework: weighting 20%
- End of semester examination: two hours weighting 80%