Close
Description
This module aims to introduce the theory of linear and generalised linear / additive models and associated data analysis. It is primarily intended for students registered on the Masters degree programmes offered by the Department of Statistical Science (including the CSML and MASS programmes).听For these students, the academic prerequisites for this module are met either through earlier compulsory study within (UG) or successful admission to (PGT) their current programme.
Intended Learning Outcomes
- have an understanding of the exponential family of distributions and their use in the formulation of generalised linear / additive models;
- be able to interpret the results of fitting such models in both a technical and non-technical manner.
Applications - The statistical methods introduced are very general and are used in almost all areas in which statistics is applied. The module will include analysis of data sets from, among other areas, biostatistics, social sciences, and economics.
Indicative Content - Multiple Linear Regression: inference techniques for the General Linear Model, applications, variable selection. Generalised Linear Models: structure incorporating an introduction to the exponential family of distributions, inference procedures. Categorical data: special cases of generalised linear models leading to logistic regression and log-linear models, use in data analysis. Introduction to non-linear modelling and mixed modelling. Introduction to Generalised Additive Models: penalised regression splines and penalised estimation.
Key Texts - Available from .
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
听