Generalised Linear Models

Module overview

This module aims to introduce students to a wide range of statistical models grouped by the unifying theory of generalized linear models: linear, logistic, multinomial, cumulative ordinal and Poisson regression, as well as log-linear models are presented, with emphasis on the underpinning theory and practical examples. Students are also exposed to the basic foundations of estimation for GLMs.

Learning and Teaching

Teaching and learning methods

Teaching will be through a combination of lectures, computer workshops and tutorials. Learning activities will include learning in lectures, which will cover explanations of the statistical modelling techniques and their use, as well as by independent study. The computer workshops will provide hands-on experience of the analysis of data and the application of the techniques introduced in the lectures, enabling you to undertake the statistical computing element of the coursework assignment. Exercises on the theory will be discussed in the tutorials.

Study time
Type	Hours
Independent Study	110
Teaching	40
Total study time	150

Resources & Reading list

General Resources

Software requirements. You will require access to R, which is available on the University’s workstations and can be downloaded to your own computer for use with your studies.

Textbooks

Faraway, J.J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC Press.

Fox, J. and Sandford, W. (2019). An R Companion to Applied Regression. Sage Publications.

Dobson, A.J. and Barnett, A.G. (2008). An Introduction to Generalized Linear Modules. CRC Press.

Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Wiley.

James, G., Witten, D., Hastie, H. and Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R. New York: Springer.

Faraway, J.J. (2015). Linear Models with R. CRC Press.

Agresti, A. (2013). Categorical Data Analysis. Wiley.

Agresti, A. (2019). An Introduction to Categorical Data Analysis. Wiley.

Fox, J. (2016). Applied Regression Analysis, Linear Models and Generalized Linear Methods. Sage Publications.

Assessment

Assessment strategy

There will be opportunities to evaluate your progress through formative assessment. The summative module assessment will consist of a piece of individual coursework and a two-hour exam. Each of these will be worth 50% of the overall mark.

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Closed book Examination	50%
Coursework	50%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Coursework	50%
Closed book Examination	50%

Repeat Information

Repeat type: Internal & External