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 delivered by a mixture of synchronous and asynchronous online methods, which may include lectures, quizzes, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study. Face-to-face teaching opportunities will be explored depending on circumstances and feasbility.

Study time
Type	Hours
Teaching	44
Independent Study	106
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

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

Fox, J (1997). Applied Regression Analysis, Linear Models, and Related Methods. Sage Publications.

Faraway, J,J (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC Press.

Agresti. A (2007). An Introduction to Categorical Data Analysis. Wiley.

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

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

Dobson. A.J (2008). An Introduction to Generalized Linear Modules. Chapman and Hall.

Assessment

Assessment strategy

There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on two online assignments.

Summative

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

Breakdown
Method	Percentage contribution
Coursework	50%
Timed Assignment	50%

Referral

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

Breakdown
Method	Percentage contribution
Assignment	50%
Coursework	50%

Repeat Information

Repeat type: Internal & External