 Description:
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Aims & Learning Objectives:
Aims To present the theory and application of normal linear models and generalised linear models, including estimation, hypothesis testing and confidence intervals. To describe methods of model choice and the use of residuals in diagnostic checking. To facilitate an in-depth understanding of the topic.Objectives On completing the course, students should be able to (a) choose an appropriate generalised linear model for a given set of data; (b) fit this model using R, select terms for inclusion in the model and assess the adequacy of a selected model; (c) make inferences on the basis of a fitted model and recognise the assumptions underlying these inferences and possible limitations to their accuracy; (d) demonstrate an in-depth understanding of the topic.
Content:
Generalised linear models: Exponential families, standard form, linear predictors and link functions, deviance. Statement of asymptotic theory for the generalised linear model, Fisher information. Vector and matrix representation.
Model building: Subset selection and stepwise regression methods. Effects of collinearity in regression variables. Model checking including residuals AIC and BIC.
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