Linear Statistical Models (L7) (971G1)
15 credits, Level 7 (Masters)
Linear modelling concerns the situation where a response variable depends on one or several variables. Despite its simplicity, it is extremely useful. Applications include:
- determining factors affecting the length of hospital stay
- estimating the reproduction number of an epidemic
- identifying elements that influence sales.
In the first part of this module, we will develop the theory of general linear models. We will be concerned with problems of estimating model parameters, finding confidence intervals as well as carrying out various statistical tests.
We will then move on to some specific models: quadratic models, analysis-of-variable models; they all belong to the family of linear models, so it is handy to have the general theory first.
The module will also help you to develop your modelling skills. While fitting models to various data sets, Questions may include:
- How is the model fitted?
- Which variables should be included in the model?
- How well does the model predict?
The module includes practical classes in the use of the statistical software R, which is used to fit models and produce statistics which help answer important practical questions. No prior knowledge of R is assumed.
20%: Coursework (Portfolio, Problem set, Report)
80%: Examination (Computer-based examination)
Contact hours and workload
This module is approximately 150 hours of work. This breaks down into about 33 hours of contact time and about 117 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.
We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2023/24. However, there may be changes to these modules in response to COVID-19, staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.