Statistical Computing and Inference

Module code: MD7453

This module provides an overview of approaches to statistical inference, both likelihood and Bayesian, applied to medical/health data and the use of statistical software packages.

Statistical Computing

You will be introduced, through an introductory lecture and self-learning guide, to basic data handling skills using the command languages of Stata and R and will have the opportunity to apply these skills.

Topics covered may include:

Writing clearly annotated and replicable programs in the course software, Stata and R, for:

  • Data entry
  • Data manipulation
  • Data analysis
  • Graphs
  • Automation of routine tasks

Statistical Inference

This teaching week introduces the different approaches to statistical inference and contrasts the traditional approach to statistics based on repeated sampling with the likelihood and Bayesian approaches. The theoretical application of those ideas requires a basic knowledge of algebra and calculus and programming is an important aspect.

Topics covered may include:

  • Likelihood, maximum likelihood estimate (MLE) and the information
  • Log likelihood curves and support intervals
  • MLE and the observed information for simple distributions
  • Bayes' theorem for both binary and general quantities
  • The nature and source of prior distributions and how to interpret posterior distributions
  • Conjugate models
  • Comparison and contrast of the different approaches to statistical inference

Learning

  • 17 one-hour lectures
  • 12 hours of practicals
  • 7 hours of tutorials
  • 114 hours of independent study

Assessment

  • Coursework: statistical computing (30%)
  • Coursework: inference (70%)