The aim of this module is to introduce the student to the analysis of data from designed experiments using the multiple regression formulation. To introduce the analysis of repeated measures data and other correlated data structures within a regression framework. The R software system (or equivalent) will be explored as a tool for fitting these models.
This module expands on the treatment of the multiple linear regression model, introduced in MATH4808, to include classical experimental design models. Advanced topics such as repeated measures and other correlated data structures are considered using estimated generalised least squares and random effects.
Review of the multiple linear regression model including categorical predictors.
Analysis of completely randomised design – the balanced and unbalanced cases – general linear hypothesis.
Multiple comparisons and least squares means.
Randomised block design, motives and methods.
Two way designs – main effects and factorial models. Testing for interaction and interpretation.
Extensions to three or more factors and simple fractional factorials
Generalised least squares and estimated generalised least squares for repeated measures and other correlated data structures.
Random effects and mixed modelling.
Lectures supported by problem-solving sessions and the use of the R statistical software package (or equivalent).
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 75 |
| Other Assessment(s) | 25 |