·      to understand how regression methods for continuous data extend to include multiple continuous and categorical predictors, and categorical response variables.

·      to provide an understanding of how this class of models forms the basis for the analysis of experimental and also observational studies.

·      to understand generalized linear models.

·      to develop familiarity with the computer package SPSS.

After completing the module students should be able to:

        understand the rationale and assumptions of linear regression and analysis of variance.

·      understand the rationale and assumptions of generalized linear models.

·      recognise the correct analysis for a given experiment.

·      carry out and interpret linear regressions and analyses of variance, and derive appropriate theoretical results.

·      carry out and interpret analyses involving generalised linear models and derive appropriate theoretical results.

·      perform linear regression, analysis of variance and generalised linear model analysis using the SPSS computer p ackage.

General Linear Models.  Simple linear regression;  one-way analysis of variance; estimation and inference; two and three-way analysis of variance; more complex designs

Generalized Linear Models: Foundations; exponential family of distributions; estimation and inference; binary response variables; normal response variables; contingency tables and log-linear models; other applications.