Multivariate Analysis
| Code | School | Level | Credits | Semesters |
| MATH3057 | Mathematical Sciences | 3 | 20 | Spring Malaysia |
- Code
- MATH3057
- School
- Mathematical Sciences
- Level
- 3
- Credits
- 20
- Semesters
- Spring Malaysia
Summary
This course is concerned with the analysis of multivariate data, in which the response is a vector of random variables rather than a single random variable. A theme running through the course is that of dimension reduction. Key topics to be covered include:
(1) principal components analysis, whose purpose is to identify the main modes of variation in a multivariate dataset;
(2) modelling and inference for multivariate data, including multivariate regression data, based on the multivariate normal distribution;
(3) classification of observation vectors into sub-populations using a training sample; canonical correlation analysis, whose purpose is to identify dependencies between two or more sets of random variables.
Further topics to be covered include: (1) factor analysis, (2) methods of clustering and (3) multidimensional scaling.
Target Students
Single Honours and Joint Honours students from the School of Mathematical Sciences.
Classes
- Two 2-hour lectures each week for 12 weeks
Assessment
- 10% Coursework 1: Individual coursework. The coursework will consist of a mixture of theoretical questions and those involving the use of a statistical package.
- 10% Coursework 2: Individual coursework. The coursework will consist of a mixture of theoretical questions and those involving the use of a statistical package.
- 80% Exam 1 (3-hour): 3 hours examination
Educational Aims
The purpose of this module is to broaden the students' knowledge of statistics by introducing them to important contemporary topics in multivariate analysis. This module builds upon the statistical ideas and methods of the course MATH2047. Students will acquire knowledge and skills of relevance to a professional and/or research statistician.Learning Outcomes
A student who completes this module successfully will be able to:
L1 - state and prove standard results relating to multivariate statistical theory;
L2 - derive multivariate statistical techniques such as principal component analysis, classification and canonical correlation analysis, clustering and multidimensional scaling, and understand and explain their properties;
L3 - derive, explain and apply methods of statistical inference for multivariate data based on the multivariate normal distribution;
L4 - explain the assumptions and limitations of multivariate statistical models and procedures;
L5 - apply multivariate models and methods to suitable datasets using a statistical environment such as R and interpret the results.