Multivariate Analysis
| Code | School | Level | Credits | Semesters |
| MATH3030 | Mathematical Sciences | 3 | 20 | Spring UK |
- Code
- MATH3030
- School
- Mathematical Sciences
- Level
- 3
- Credits
- 20
- Semesters
- Spring UK
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:
- principal components analysis, whose purpose is to identify the main modes of variation in a multivariate dataset;
- modelling and inference for multivariate data, including multivariate regression data, based on the multivariate normal distribution;
- 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
- factor analysis,
- methods of clustering and
- multidimensional scaling.
Target Students
Single Honours and Joint Honours students from the School of Mathematical Sciences. MSc students should take MATH4068 instead.
Assessment
- 10% Coursework 1: A combination of a take-home exercise sheet and some data analysis using a suitable statistical package.
- 10% Coursework 2
- 80% Exam 1 (3-hour): Written examination.
Assessed by end of spring semester
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 thecourse MATH2011. 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.
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.