Linear Statistical Models (L6) (G1107)
Linear Statistical Models (L6)
Module G1107
Module details for 2022/23.
15 credits
FHEQ Level 6
Module Outline
Full-rank model (multiple and polynomial regression); estimation of parameters, analysis of variance and covariance. Model checking. Comparing models; model selection. Transformation of response and regressor variables. Models of less than full rank (experimental design); analysis of variance, hypothesis testing; contrasts. Simple examples of experimental designs; introduction to factorial experiments. Use of a computer statistical package to analyse real data sets.
Module learning outcomes
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, using transformations and variable selection techniques.
Understand the benefits of designed experiments, select and carry out statistical analyses and report conclusions clearly.
Use statistical software to fit regression and analysis of variance models, compute additional statistics and construct diagrams.
| Type | Timing | Weighting |
|---|---|---|
| Computer Based Exam | Semester 1 Assessment | 80.00% |
| Coursework | 20.00% | |
| Coursework components. Weighted as shown below. | ||
| Portfolio | T1 Week 11 | 40.00% |
| Report | T1 Week 11 (1 hour) | 30.00% |
| Computer Based Exam | T1 Week 8 (1 hour) | 30.00% |
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 | 30303030303 |
| Autumn Semester | Practical | 1 hour | 01010101010 |
| Autumn Semester | Lecture | 2 hours | 10101010101 |
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, Convenor
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