This module is concerned with the application of linear models to the analysis of data. The underlying assumptions are discussed and general results are obtained using matrices. The standard approach to the analysis of normally distributed data using ANOVA is introduced. Methods for the design and analysis of efficient experiments are introduced. The general methodology is extended to logistic regression and the analysis of multidimensional contingency tables.
Learning Outcomes
On completion of the module students should be able to:
- calculate confidence intervals for parameters and prediction intervals for future observations;
- understand how to represent a linear model in matrix form;
- check model assumptions and identify influential observations;
- identify simple designed experiments;
- construct factorial experiments in blocks;
- adapt linear models to fit growth curves;
- carry out logistic regression;
- analyze cross-tabulated data using log linear models;
- analyse linear models using R.
- Module Supervisor: Stella Hadjiantoni
- Module Supervisor: Danilo Petti