Dr Mona Azadkia

This course is available on the BSc in Actuarial Science, BSc in Data Science, BSc in Financial Mathematics and Statistics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

This course is not capped, any student that requests a place will be given one

Students must have completed:

Probability, Distribution Theory and Inference (ST202) AND Mathematical Methods (MA100) or equivalent.

It is assumed students have taken at least a first course in linear algebra.

Previous programming experience is not required  but students who have no previous experience in R must complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=8714)

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.

This course will be delivered through a combination of lectures and classes totalling a minimum of 30 hours in Autumn Term.

This course includes a reading week in Week 6.

• Dobson, A.J. (2008). An Introduction to Generalized Linear Models.
• Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models
• Frees, E.W. (2010). Regression Modeling with Actuarial and Financial Applications

Exam (70%, duration: 2 hours) in the January exam period.
Coursework (30%) in the AT.