| Unit name | Generalised Linear Models |
|---|---|
| Unit code | MATH35200 |
| Credit points | 10 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Liverani |
| Open unit status | Not open |
| Pre-requisites | |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
We study methods for the analysis of data in which one variable, the response, is influenced systematically by one or more explanatory variables, which could be qualitative or quantitative in nature, in addition to the presence of random variation. In contrast to well-known and traditional methods involving linear models and normal variation (as studied in earlier units), here we depart from linearity and normality, and need the principle of maximum likelihood to fir our models, instead of relying on least squares. The topics discussed will be: Generalised linear models: extensions of the ideas of linear modelling to deal with situations where the response variable takes integer or categorical values. These methods are particularly important in biomedical applications. Survival analysis: an introduction to regression models for lifetime data, used in clinical trials and industrial testing.
