Statistical methods are used to analyse data in a wide variety of fields (e.g. engineering, medicine, agriculture, business, economics, psychology, genetics, criminology, the social sciences). While statistical theory can be helpful in analysing such data, its direct application may be limited by practical problems. For example, some of the data may be missing, some observations may be inconsistent with the rest of the data, the standard assumptions (e.g. normality) may fail, and the standard methods may not answer the important questions. The best way to learn how to deal with these practical problems is to gain experience in analysing real data. This unit provides that experience through case studies and projects. The emphasis is on applying statistical methods to interesting practical problems rather than on the theory behind the methods.

The unit covers applications of a number of widely used statistical techniques selected from generalised linear models, nonlinear regression models, advanced regression topics, survival analysis, non-parametric statistics, multivariate analysis, and time series analysis. Furthermore, throughout the unit a large emphasis is placed on data visualisation techniques.

Students are able to (1) apply statistical reasoning to analyse the essential structure of problems in various fields of human endeavour; (2) extend their knowledge of statistical techniques and adapt known solutions to different situations; (3) communicate effectively with others and present results in a logical and coherent fashion; and (4) produce high quality and appropriate data visualisations using a variety of techniques and software packages.