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STAT412 Generalised Linear Models

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The theory and application of generalised linear models. The emphasis will be on application, with a focus on real data analysis using R.

We consider the theory and use of generalised linear models. This paper will include theory, but primarily emphasise application with a focus on parameter interpretation and hypothesis testing in real data analysis using R.

Paper title Generalised Linear Models
Paper code STAT412
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $1,154.90
International Tuition Fees (NZD) $4,801.79

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STAT 341, STAT 362
For any student who has completed STAT 310 (or equivalent) and wishes to know more about this important framework for analysing many types of data. A working knowledge of these models is extremely valuable for any practising statistician.

Dr Matthew Parry or Professor Katrina Sharples

Teaching staff

Professor Katrina Sharples

Paper Structure
  • Introduction to generalised linear models
  • Nominal and ordinal logistic models
  • Poisson models
  • Overdispersion
  • Zero-inflated models
  • Modelling rates using an offset
  • Log-linear models
  • Gamma and lognormal models
  • Quasi-likelihood
  • Generalised additive models
  • Survival models
Teaching Arrangements
Three contact hours per week.
No required texts.

Useful references:
  • Collett (1991) Modelling binary data, Chapman and Hall
  • Dobson (2002) An introduction to generalized linear models, Chapman and Hall
  • Faraway (2006) Extending the linear model with R,Chapman and Hall
  • Krzanowski (1998) An introduction to statistical modelling, Arnold
  • McCullagh and Nelder (1989) Generalized linear models, Chapman and Hall
Course outline
View course outline for STAT 412
Graduate Attributes Emphasised
Communication, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes.
Learning Outcomes
Students who successfully complete the paper will develop an ability to analyse different types of data.

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Semester 2

Teaching method
This paper is taught On Campus
Learning management system