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GENERALISED LINEAR MODELS (R)

Module code: ST834
Credits: 5
Semester: 2
Quota: Restricted entry, contact department for details before registering
Department: MATHEMATICS AND STATISTICS
International:

	Overview
	Overview -------- Transformation of response and predictors, Box-Cox method, variance stabilizing transformation. Collinearity and variance inflation factors. Model selection: forwards, backwards and stepwise. All possible regressions, Mallow's Cp and PRESS. Categorical data methods. Generalized linear models, exponential families. Logistic regression for binary responses and Binomial counts. Loglinear models for Poisson counts, two- and three-way tables. Lectures and tutorials as for ST304.

Overview

Overview
--------
Transformation of response and predictors, Box-Cox method, variance stabilizing transformation. Collinearity and variance inflation factors. Model selection: forwards, backwards and stepwise. All possible regressions, Mallow's Cp and PRESS.
Categorical data methods. Generalized linear models, exponential families. Logistic regression for binary responses and Binomial counts. Loglinear models for Poisson counts, two- and three-way tables.

Lectures and tutorials as for ST304.

	Learning Outcomes
	On successful completion of the module, students should be able to: Build regression models. Assess need for transformations. Use model selection tools. Examine data using two- and three-way contingency tables. Fit and interpret generalised linear models.

Teaching & Learning methods

Assessment

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? No

	Pre-Requisites
	This module is for Registered Research students. Permission is required from the Head of the Mathematics and Statistics Department to take this module.

	Timetable
	This timetable is not currently available, please contact the Mathematics And Statistics Department.

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Courses / Module

GENERALISED LINEAR MODELS (R)