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Unit information: Generalised Linear Models 34 in 2015/16

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Unit name Generalised Linear Models 34
Unit code MATHM5200
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 1B (weeks 7 - 12)
Unit director Dr. Heather Battey
Open unit status Not open

MATH 20800 Statistics 2, MATH 35110 Linear Models



School/department School of Mathematics
Faculty Faculty of Science

Description including Unit Aims

Unit aims

To study both theoretical and practical aspects of statistical modeling, to develop the expertise in selecting and evaluating the model and interpreting the results.

General Description of the Unit

The course is focused on multivariate regression methods with univariate independent outcomes that can take on continuous or categorical values.The topics discussed include:

Model selection, parameter estimation, diagnostics, results interpretation. Methods for estimating the standard error. Regression models for lifetime data. Relation to Other Units

This unit builds on the basic ideas of linear models introduced in Statistics 1 (MATH 11400) and Linear Models (MATH 35110), and extends them to deal with more general specifications.

Further information is available on the School of Mathematics website:

Intended Learning Outcomes

Learning Objectives

By the end of the unit, the student should have a good understanding of

  • principles of statistical modelling: response and explanatory variables, systematic and random variation, independence and conditional independence;
  • methods of inference: maximum likelihood;
  • methodology of generalized linear models and survival analysis;
  • advance use of the statistical software system (R).

Transferable Skills

The ability to analyze relatively complex data sets that includes exploratory data analysis, model formulation, statistical computing, model evaluation, diagnostics and the ability to interpret the results for the general audience.

Teaching Information

Lectures, examples and homework problems.

Assessment Information

80% Examination and 20% Coursework.

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

Reading and References

The range of topics covered in the unit is rather broad. Students might find the following textbooks useful

  • W J Krzanowski, An Introduction to Statistical Modelling, Arnold, 1998.
  • P McCullagh, J A Nelder, Generalized Linear Models, Chapman and Hall, 1983.
  • A C Dobson, Introduction to statistical modelling, Chapman and Hall, 1983.
  • D R Cox and D Oakes, Analysis of survival data, Chapman and Hall, 1984.

Other useful references include

  • W N Venables and B D Ripley, Modern applied statistics with S-Plus, Springer, 1994.
  • J Fox. An R and S-Plus Companion to Applied Regression, Sage Publications, 2002.
  • B A Everitt, T Hothorn, A Handbook of Statistical Analysis Using R, Chapman&Hall, 2006.