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Linear Models and Generalised Linear Models are very widely used statistical tools. Linear models allow us to model data with normally distributed errors and generalised linear models extend these methods to a wider family of distributions. While students are expected to have obtained some understanding of linear regression techniques in previous courses, this course offers a more complete introduction to linear models and their application, then, building on this, extends into generalised linear models. The key functions of linear models are for describing the relationships between variables and predicting outcomes and so inference methods will be addressed in some detail. Finally, as models only give useful information when they provide an accurate reflection of the 'real world', various diagnostic tests on the appropriateness and goodness of fit of various models will be introduced. This course has relevance to all students seeking to pursue a career involving applied statistics.

This course introduces and extends the student's knowledge of linear models. The mathematical development of these models will be considered; however, the focus will be on practical applications. The statistical program R will be introduced and used throughout the course. The topics include developing multiple regression models, testing hypotheses for these models, selecting the 'best' model, diagnosing problems in model fit, shrinkage methods, developing generalised linear models, and a range of applications of generalised linear models including logistic, Poisson and log-linear models. Analysis of different statistical models are practised using the statistical software package through the R and RStudio.