Aims and Fit of Module

The module aims to enable students to understand how regression methods for continuous data extend to include multiple continuous and categorical predictors and categorical response variables. It also provides an understanding of how this class of models form the basis for the analysis of experimental and also observational studies to understand generalized linear models. It finally enables students to understand how the Bayesian framework is applied to linear regression model.

Learning outcomes

A. Demonstrate knowledge and understanding of basic principles of R programming language
B. Demonstrate understanding of the significance of linear regression models and ANOVA tables
C. Show understanding of the rationale and assumptions of linear regression models
D. Show understanding of the rationale and assumptions of generalized linear models.
E. Carry out and interpret linear regressions and analyses of variance, and derive basic theoretical results.
F. Carry out and interpret analyses involving generalized linear models and derive basic theoretical results.
G. Show understanding of how the Bayesian framework is applied to linear regression models.

Method of teaching and learning

The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments.
This module will be delivered by a combination of formal lectures, seminars, tutorials as well as private studies.