Aims and Objectives
Having successfully completed this module you will be able to:
- Use models to investigate the association structure between variables
- Summarise data with an appropriate statistical model
- Interpret the results of modelling
- Knowledge and understanding of the general theory of generalised linear models
- Check the models
- Use the statistical software packages to fit statistical models and develop computing skills
- Use models to describe the relationship between a response and a set of explanatory variables, in particular for datasets arising in official statistics
- Exponential family of distributions and properties (Normal, Binomial and Poisson)
- Review of models for normal response models
- Tables, measures of association and odds ratios
- Logistic regression
- Multinomial logistic regression
- Ordinal regression
- Poisson Regression and models for rates
- Introduction to random effects models
- Log linear models
- Goodness of fit Statistics, Likelihood Ratio Test
Learning and Teaching
Teaching and learning methods
Lectures with integrated computing workshops and tutorials.
|Total study time||100|
Resources & Reading list
Fox, J. (1997). Applied Regression Analysis, Linear Models, and Related Methods. Sage Publications.
Agresti, A. (2002). Categorical Data Analysis. Wiley.
Dobson, A. J. (2001). An Introduction to Generalized Linear Models. Chapman & Hall.
Agresti, A. (2007). An Introduction to Categorical Data Analysis. Wiley.
Fox, J. (2002). An R and S-PLUS Companion to Applied Regression. Sage Publications.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External