Introductory description

This module runs is optional for students with their home department in Statistics . It may be possible for students from other courses to take this module as an Unusual Option, subject to permission from the module leader and the home department.

Pre-requisites: ST221 Linear Statistical Modelling or ST231 Linear Statistical Modelling with R.

Leads to: further modules that require statistical modelling, especially ST332 Medical Statistics.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Introduce Generalised Linear Models as an extension of the standard linear model from ST221 under the umbrella of maximum likelihood estimation.

2. Covering model selection, parameter uncertainty and model validation for GLMs.

3. Extend this framework further to models that can handle overdispersion and be more robust to model selection e.g. random effects and quasi-likelihood models.

Learning outcomes

By the end of the module, students should be able to:

• identify an appropriate modelling approach for different types of response variable.
• select a model and analyse it in a principled way and verify the model assumptions.
• give clear uncertainty quantification for both parameter estimates as well as for model predictions.
• understand the key modelling assumptions and have a good understanding of how to make basic extensions to the standard GLM when issues like overdispersion are present.
• both apply and understand the theory and computation of GLMs

Subject specific skills

• Demonstrate facility with rigorous statistical modelling methods.

• Evaluate, select and apply appropriate statistical modelling techniques.

• Demonstrate knowledge of and facility with formal statistical modelling concepts, both explicitly and by applying them to the solution of problems.

• Create structured and coherent arguments communicating them in written form. 

• Construct logical arguments with clear identification of assumptions and conclusions.

• Reason critically, carefully, and logically.

Transferable skills

• Problem solving: Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions. Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious. Know how to learn from failure.

• Self awareness: Reflect on learning, seeking feedback on and evaluating personal practices, strengths and opportunities for personal growth.

• Communication: Present arguments, knowledge and ideas, in a range of formats.

• Professionalism: Prepared to operate autonomously. Aware of how to be efficient and resilient. Manage priorities and time. Self-motivated, setting and achieving goals, prioritising tasks.