Please read our student and staff community guidance on COVID-19
Skip to main content Skip to navigation

APTS module: Statistical Modelling

Module leader: D C Woods and Antony Overstall

Please see the full Module Specifications PDF file document for background information relating to all of the APTS modules, including how to interpret the information below.

Aim: The main aim of this module is to introduce important general aspects of statistical modelling, including Bayesian modelling, and to introduce some fundamental aspects of data collection. A broad range of specific, commonly-used types of model will also be encountered.

Learning outcomes: After taking this module, students should --- for topics listed below which are included in the module --- understand the issues (why this is important), the terminology, the statistical principles associated with this aspect of modelling, and sufficient theory to deal with simple examples; and they will have gained some practical hands-on experience in more complex examples.

Prerequisites: Preparation for this module should (re-)establish familiarity with linear and generalized linear models, and with likelihood and Bayesian inference. Students who are familiar with (for example) chapters 4, 8, 10 and 11 of Davison (2003) ``Statistical Models'' will be very well prepared (and will already know something of the areas to be covered in the module).


  • Principles and practice of model selection;
  • Random-effects/hierarchical/mixed models;
  • The role of conditional independence in modelling;
  • Data collection and an introduction to design of experiments.

Assessment: Exercises set by the module leader, which will include some practical data analysis and statistical modelling.