Linear Statistical Models (L7) (971G1)
Linear Statistical Models (L7)
Module 971G1
Module details for 2023/24.
15 credits
FHEQ Level 7 (Masters)
Module Outline
Linear modelling concerns the situation where a response variable depends on one or several variables. Despite its simplicity, it turns out to be extremely useful. Here are some applications:
• 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’ll develop the theory of general linear models. In particular, we’ll be concerned with problems of estimating model parameters, finding confidence intervals as well as carrying out various statistical tests. We’ll 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 ready first!
The module will also help you to develop your modelling skills. While fitting models to various data sets, we shall pay particular attention to questions such as:
• 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.
Module learning outcomes
Comprehensively understand the theory of the general linear model and derive distributional results relating to estimators.
Apply the general linear model of full rank to a variety of applications, with transformations and variable selection techniques, and evaluate the appropriateness of model application.
Systematically understand the benefits of designed experiments, select and carry out statistical analyses independently and report conclusions clearly to both specialists and non-specialists.
Use statistical software to fit regression and analysis of variance models, compute additional statistics and construct diagrams.
| Type | Timing | Weighting |
|---|---|---|
| Unseen Examination | Semester 1 Assessment | 80.00% |
| Coursework | 20.00% | |
| Coursework components. Weighted as shown below. | ||
| Problem Set | T1 Week 8 | 30.00% |
| Report | T1 Week 11 | 30.00% |
| Portfolio | T1 Week 11 | 40.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Lecture | 1 hour | 10101010101 |
| Autumn Semester | Lecture | 2 hours | 11111111111 |
| Autumn Semester | Practical | 1 hour | 01010101010 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Minmin Wang
Assess convenor
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