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MT4607 GENERALIZED LINEAR MODELS AND DATA ANALYSIS
Aims- To demonstrate the power and elegance of unifying a large number of simple statistical models within the framework of the generalised linear model.
- To train a student in the interpretation, analysis and reporting of data, when a single response measurement is to be interpreted in terms of one or a number of other variables and factors.
ObjectivesBy the end of the course students are expected to:
- be able to formulate appropriate problems as generalised linear models;
- understand and prove the basic properties of least squares estimators;
- understand the assumption of the models and be able to test these;
- understand the distribution results that allow competing models to be compared;
- carry out exploratory and confirmatory analyses using R;
- write a comprehensible report describing the analysis of a data set and the conclusions which can validly be drawn from it;
- interpret and criticise R analyses of data .
SyllabusGeneralised linear models, and ordinary linear models.
How to build models (with design matrices).
The exponential family of distributions (Normal, binomial, Poisson etc.).
Checking models: residuals etc.
How GLMs are fitted to data.
Dealing with overparameterized models.
Inference (sampling distribution of parameters and analysis of deviance).
Models for contingency tables (and why the canonical link is interesting).
The geometry of least squares and orthogonality.
Projectors, sums of squares and ANOVA.
TextbooksAn Introduction to Statistical Modelling: W Krzanowski, Arnold.
An Introduction to Generalized Linear Models: A J Dobson, Chapman & Hall.
AssessmentProject = 20%, 2 Hour Examination = 80%
PrerequisitesMT2004 and MT3501 (as co-requisite)
AvailabilityAcademic year 2012/13 in semester 1 at 9
LecturerDr A Overstall
Click here for access to past examination papers for this module.
Click here to see the University Course Catalogue entry.
Revised: PMH (April 2012)