Linear Statistical Models
Module code: G1107
15 credits in autumn semester
Teaching method: Lecture, Practical
Assessment modes: Coursework, Unseen examination
- 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 SAS to fit regression and analysis of variance models, compute additional statistics and construct diagrams.