You will study 180 or 185 credits in total during your Statistics MSc. A standard module is typically worth 15 credits and the research project is worth 60 credits. These are the modules studied in 2018. If you are starting in September 2019, these will give you a flavour of the modules you are likely to study. All Modules are subject to change.
Independent Learning and Skills Project - 15 credits
Students will be able to develop a systematic search strategy to find material on a given topic, using Mathematical word processing and evaluation of material, referencing conventions.
Statistical Computing - 15 credits
The use of computers in mathematics and statistics has opened up a wide range of tech- niques for studying otherwise intractable problems and for analysing very large data sets."Statistical computing" is the branch of mathematics which concerns these techniques for situations which either directly involve randomness, or where randomness is used as part of a mathematical model.
Dissertation in Statistics - 60 credits
Each student will discuss with an individual supervisor a suitable research project. The title and objectives of the project will be approved by the Programme Manager.
Optional modules include
Linear Regression and Robustness - 15 credits
This module will examine ways of predicting one particular variable from the remaining measurements using the linear regression model. The general theory of linear regression models will be covered, including variable selection, tests and diagnostics. Robust methods will be introduced to deal with the presence of outliers.
Time Series and Spectral Analysis - 15 credits
The module will concentrate on techniques for model identification, parameter estimation, diagnostic checking and forecasting within the autoregressive moving average family of models and their extensions.
Multivariate and Cluster Analysis - 15 credits
Multivariate datasets are common to all research areas: it is typical that experimental units are measured for (or questioned about) more than one variable at a time. This module covers the extension of univariate statistical techniques for continuous data to a multivariate setting and introduces methods designed specifically for multivariate data analysis (cluster analysis, principal component analysis, multidimensional scaling and factor analysis).
Stochastic Financial Modelling - 15 credits
Financial investments such as stocks and shares are risky: their value can go down as well as up. To compensate for the risk in a fair market, a discount is needed. This module will develop the necessary probabilistic tools to enable investors to value such assets.
Generalised Linear and Additive Models - 15 credits
Linear regression is a tremendously useful statistical technique but is very limited. Generalised linear models extend linear regression in many ways - allowing us to analyse more complex data sets. In this module we will see how to combine continuous and categorical predictors, analyse binomial response data and model count data.
Risk Management - 15 credits
This module covers the different sorts of risk to which financial investments are exposed, basic and sophisticated derivates commonly used for hedging, expected utility theory, models of incomplete markets, Value-at-Risk and other risk measures, credit risks and credit derivatives, methods to determine the effectiveness of a hedge, stress-testing of risky investment portfolios.