|Delivery Type||Delivery length / details|
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||open book exam at end of course||100%|
At the end of this module, the student should be able to have a meaningful dialogue with a statistician on experimental design, statistical analysis, and interpretation of a statistical analysis. He/she should not necessarily expect to be able to select the most appropriate analysis for a given situation as this can only come through experience.
Another objective of the module is to enable a biological research worker to have a meaningful dialogue with a professional statistician. It cannot be too strongly emphasised that for anything other than the simplest, whenever an experiment is being designed it should be done in conjunction with a statistician. This is to ensure that the data collected can be analysed in such a way that the questions being asked of the data can be answered as unambiguously as possible. This module aims to introduce a research worker to the concepts and terminology of statistical science, as well as to detailed methods in the three broad areas which are of most use to research biologists.
Experimental Design and Analysis of Variance (ANOVA): General principles of experimental design. One-way ANOVA. Comparisons - multiple range tests, orthogonal comparisons, comparisons by regression components (after regression part of the course). Variance heterogeneity and data transformation. The randomised block design. Two-factor ANOVA. More briefly, simply to give a flavour of what designs and analyses can be done: three- and four-factor ANOVAs; incomplete factorial designs; nested designs; split-plot designs; Model I & Model II ANOVAs.
Regression Analysis: The idea of fitting a straight line (or curve) to data and performing various tests on the fitted relationship. Straight line regression - fitting, test of significance, tests on the gradient, confidence band, testing linearity. Curvilinear regression - examples where transformation achieves linearity, the distinction between linear and non-linear regression in the statistical sense, polynomial curves, other curves. Multiple regression. Generalised linear models (a mention of).
Multivariate Methods: The concept of multivariate data and analysis. Introduction to matrix algebra. Summarisation of multivariate data; Some commonly used multivariate methods: Principal Component Analysis, Canonical Variate (Discriminant Function) Analysis, Multivariate Analysis of Variance.
This module is at CQFW Level 7