| Unit name | Linear Models |
|---|---|
| Unit code | MATH35110 |
| Credit points | 10 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Beaumont |
| Open unit status | Not open |
| Pre-requisites | |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
This unit explores the role of linear models as a statistical tool for modelling data. Theoretical aspects of such models are explored but the emphasis is on strategies and methodology for modal selection, estimation, inference and checking. Models covered include simple and multiple regression, and one- and two-way analysis of variance for factorial experiments. Inference will be based largely on the least-square criterion, exploiting the Gauss-Markov theorem, but connections will also be made with likelihood-based approach. The use of R for modelling data via linear models will be integral to the course.
