ST300      Half Unit
Regression and Generalised Linear Models

This information is for the 2019/20 session.

Teacher responsible

Dr Philip Chan


This course is compulsory on the BSc in Statistics with Finance. 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.


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.  


20 hours of lectures, 7 hours and 30 minutes of classes and 5 hours of classes in the MT.

Students will be given their assessed project to start on sometime from Week 7-9 which is due in at the beginning of LT.

This course operates a reading week in Week 6.

Indicative reading

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


Exam (85%, duration: 2 hours) in the summer exam period.
Project (15%) in the LT.

Student performance results

(2016/17 - 2018/19 combined)

Classification % of students
First 43.1
2:1 20.2
2:2 19.1
Third 12.2
Fail 5.3

Key facts

Department: Statistics

Total students 2018/19: 71

Average class size 2018/19: 18

Capped 2018/19: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Problem solving
  • Application of numeracy skills
  • Specialist skills