- 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 skills in using an appropriate statistical software package.

(LO1) General linear model: explain the rationale and assumptions, derive appropriate theoretical results.

(LO2) Implement and interpret linear regression.

(LO3) Implement and interpret analysis of variance.

(LO4) Generalized linear models: explain the rationale and assumptions, implement, and interpret, derive appropriate theoretical results.

(LO5) Identify the correct analysis for a given experiment.

(LO6) Apply linear regression, analysis of variance and generalised linear model analysis using an appropriate statistical software package and interpret the results.

(LO7) Perform additional research of statistical methods beyond the material covered in videos or research history of maths related to this module.

- 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.