ST4GLM-Generalised Linear Models

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites: ST1PS Probability and Statistics and ST2LM Linear Models or ST2LMD Linear Models and Data Analysis
Non-modular pre-requisites:
Co-requisites:
Modules excluded: ST3GLM Generalised Linear Models
Current from: 2020/1

Module Convenor: Prof Sue Todd

Email: s.c.todd@reading.ac.uk

Type of module:

Summary module description:

This module extends the linear model, introducing the generalised linear modelling framework for analysing non-normal data, with particular focus on commonly used models such as logistic regression and log-linear models.

Aims:

Statistical modelling is an essential feature of investigations into both industrial and biological processes concerning plants and animals (including human beings). From such processes data of many types naturally evolve. In this module we are concerned with data expressed as proportions (e.g. proportion of seeds germinating in a given batch or proportion of components failing) or dataÌý in the form of counts (e.g. the number of animals of a particular species found in a certain locality). The aim of the module is to explore the statistical modelling approaches of relevance for theÌý analysis of these types of data.

Assessable learning outcomes:

By the end of the module it is expected that the student willÌý have:

• familiarity with the logistic regression model for binary data analysis and the log-linear model for count data;


• an ability to use computer software to fit and assess regression models for binomial and Poisson data, and to interpret results;


• an awareness of the relationship between contingency tables and PoissonÌý Ìýmodels.


• This mod ule will be assessed to a greater depth than the excluded module ST3GLM.

Additional outcomes:

Outline content:

Logistic modelling of binary data: binomial distribution, parameter estimation in the linear logistic model; bioassay and estimation of ED50; model comparison using analysis of deviance. Log-linear models for count data; multinomial models; analysis of contingencyÌý tables.

Brief description of teaching and learning methods:

Lectures supported by tutorials andÌýpracticals.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	20
Practicals classes and workshops	3
Guided independent study:	77
Ìý	Ìý	Ìý	Ìý
Total hours by term		0	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written exam	70
Set exercise	30

Summative assessment- Examinations:

2 hours.

Summative assessment- Coursework and in-class tests:

Two assignments which will involve independent research.

Formative assessment methods:

Problem sheets and practicals.

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

An overall mark of at least 50%.

Reassessment arrangements:

One examination paper of 2 hours duration in August/September - the resit module mark will be the higher of the exam mark (100% exam) and the exam mark plus previous coursework marks (70% exam, 30% coursework).

Additional Costs (specified where applicable):

Last updated: 17 April 2020