This course will provide an overview of general purpose statistical modelling and its implementation in R using a range of examples including some from the medical/public health area.
Provide a brief review of what statistical modelling involves, what can (and can\'t be) expected from it, and what toolbox of inferential approaches it has to draw upon (including use of likelihood, Bayesian/Markov Chain Monte Carlo (MCMC) methods, and resampling ideas).
Describe in outline some examples of Linear, Generalised Linear models and Generalised Linear Mixed Models - the most widely used classes of parametric statistical models (including both traditional likelihood-based and Bayesian approaches to fitting and assessing such models).
Describe in outline some examples of non-parametric modelling (and associated approaches to fitting and assessing such models).
Throughout the course available software to implement the various modelling methods will be illustrated (including a short introduction to the use of R and to the use of WinBUGS (for Bayesian modelling) and also to links between R and WinBUGS.