This module explores Generalised Linear Models (GLMs) – a powerful and widely used extension of linear regression for analysing real-world data. You will learn how GLMs provide a unifying framework for modelling binary outcomes, counts, and other types of data where the usual assumptions of normality do not hold.

Through a combination of lectures and hands-on computer labs in R, you will develop both the theoretical understanding and the practical skills to fit, interpret, and assess GLMs. The focus will be on working with real data sets from a variety of fields, learning how to choose appropriate link functions, interpret model coefficients, assess model fit, and communicate results clearly.

By the end of the module you will be able to:

• Recognise when GLMs are appropriate and select the right model for a given problem.

• Fit and interpret logistic regression, Poisson regression, and log-linear models.

• Use residual diagnostics and model selection criteria to assess and improve model fit.

• Apply your knowledge to real-world data analysis tasks using R and present your results effectively.