Many real-world problems involve analysing data sets that are not normally distributed. For example, binomial data in the form of presence/absence recordings, Poisson data measured as counts of rare events such as car accidents, Gamma data for measurements of rainfall and Weibull data for the expected lifetimes of machinery. This unit provides experience in analysing such observations. The majority of the unit concentrates on the presentation and analysis of such data sets. Generalised Linear Models (GLMs) are used to incorporate explanatory variables into the analyses. In developing these skills students are trained in an appropriate statistical software package. The unit also provides a rudimentary understanding of probability and statistics necessary for applying the likelihood theory for estimating these models.

Students are able to (1) demonstrate their knowledge of fundamental concepts in probability and statistics; (2) apply statistical models to real-world problems for data that are not normally distributed; (3) use computer package(s) for fitting such models to data; and (4) communicate the results of these analyses effectively to non-statisticians.