ST300 Half Unit
Regression and Generalised Linear Models
This information is for the 2020/21 session.
Teacher responsible
Dr Philip Chan
Availability
This course is available on the BSc in Actuarial Science, BSc in Business Mathematics and Statistics, BSc in Financial Mathematics and Statistics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.
Pre-requisites
Students must have completed:
EITHER Probability, Distribution Theory and Inference (ST202) OR Probability and Distribution Theory (ST206)
AND Mathematical Methods (MA100) or equivalent.
It is assumed students have taken at least a first course in linear algebra.
Course content
A solid coverage of the most important parts of the theory and application of regression models, and generalised linear models. Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals. Model choice, fitting and validation.
The use of the statistics package RStudio will be an integral part of the course. The computer workshops revise the theory and show how it can be applied to real datasets from finance and insurance including CAPM, and actuarial models of claims on insurance policies.
Students will sit a summer term closed-book exam.
Teaching
This course will be delivered through a combination of classes and lectures totalling a minimum of 30 hours across Michaelmas Term. This year, some or all of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos.
This course includes a reading week in Week 6 of Michaelmas.
Indicative reading
Dobson, A.J. (2008). An Introduction to Generalized Linear Models.
Frees, E.W. (2010). Regression Modeling with Actuarial and Financial Applications
Wickham, H, and Grolemund, G. (2017). R for Data Science. O'Reilly. Available online at http://r4ds.had.co.nz
Assessment
Exam (85%, duration: 2 hours) in the summer exam period.
Project (15%) in the LT.
Student performance results
(2017/18 - 2019/20 combined)
| Classification | % of students |
|---|---|
| First | 44.2 |
| 2:1 | 26.5 |
| 2:2 | 16.4 |
| Third | 8.8 |
| Fail | 4 |
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Key facts
Department: Statistics
Total students 2019/20: 99
Average class size 2019/20: 25
Capped 2019/20: No
Value: Half Unit
Personal development skills
- Problem solving
- Application of numeracy skills
- Specialist skills
