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|
|Teaching period||Not offered in 2020|
|Domestic Tuition Fees (NZD)||$1,142.40|
|International Tuition Fees (NZD)||$4,661.93|
- STAT 341, STAT 362
- For any student who has completed STAT 341 and STAT 362 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 Dr Ting Wang
- More information link
- View further information for STAT 412
- Teaching staff
- Dr Ting Wang
- Paper Structure
- Introduction to generalised linear models
- Nominal and ordinal logistic models
- Poisson models
- Zero-inflated models
- Modelling rates using an offset
- Log-linear models
- Gamma and lognormal models
- Generalised additive models
- Survival models
- Teaching Arrangements
- Three contact hours per week.
- No required texts.
- 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.