Module Identifier  BSM1210  
Module Title  STATISTICS FOR RESEARCH BIOLOGISTS  
Academic Year  2006/2007  
Coordinator  Dr David R C Causton  
Semester  Semester 1  
Other staff  Dr David R C Causton  
PreRequisite  GCSE Mathematics or equivalent. Some previous knowledge of statistics highly desirable, but not essential  
Course delivery  Lecture  20 x 1.5 hours  
Seminars / Tutorials  
Assessment 

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 emphaissed 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 ambiguously 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., Oneway 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. Twofactor ANOVA. More briefly, simply to give a flavour of what designs and analyses can be done; three and fourfactor ANOVAs; incomplete factorial designs; next designs; splitplot designs; incomplete block designs.
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 nonlinear 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 (Multiple Discriminant) Analysis, Multivariate Analysis of Variance.
This module is at CQFW Level 7