Module Identifier RSM0810  
Academic Year 2003/2004  
Co-ordinator Dr Malcolm H Leitch  
Semester Semester 2 (Taught over 2 semesters)  
Other staff Miss Sylvia G Lutkins  
Course delivery Lecture   36 Hours 18 x 2 hour combined lectures/practical sessions  
  Practical   12 Hours 6 x 2 hour computer workshops  
Assessment TypeAssessment Length/DetailsProportion
Semester Exam3 Hours Open book written examination Outcomes assessed 1, 2, 330%
Semester Assessment Assignment Outcomes assessed: 1, 2, 3  40%
Semester Assessment Continuous assessment Outcomes assessed: 1, 2, 3, 4  30%
Supplementary Assessment data analysis assignment outcomes 1, 2, 3, 4  100%

Learning outcomes

On completion of this module students will be able to:

1. Identify appropriate methods of analysis for different types of data.

2. Analyse data using a range of statistical analyses.

3. Interpret the results of data analyses.

4. Apply statistical knowledge in the content of postgraduate reserch.

Brief description

A practical course with short explanatory lectures and practical demonstration. The content will include probability, normal distribution, parametric tests including t-tests, one way and multi way ANOVA. Correlation and regression. Non parametric methods will include chi-square analysis of frequencies, contingency tables, Mann-Whitney U test, Spearman rank correlation. Computer packages will be used in practical/demo sessions to reinforce the lecture material. To some extent the exact course content will vary in response to student ability and need.

Module Outline
NB This module will be taught in two parts:-

Part 1 - Semester 1, is taught joint with undergraduates module RS21810. All the assessment associated with module is compulsory to all students, attendance at lectures/practicals is optional.

Part 2 - Semester 2, is taught exclusively to MSc students. Both assessment and attendance at lectures/practicals is compulsory.

Transferable skills

.1 Independent project work
   Students are required to collect, analyse and interpret data for the assignment.

.2 IT and information handling
   Students will use statistical packages to analyse data.

.3 Use and analysis of numerial information
   The prime basis of the module

.4 Writing in an academic context
   Required for the assignment

.7 Self management
   For the assignment project, students will need to be responsible for organising their own time management

Reading Lists

Campbell, R C (1989) Statistics for Biologists 3rd. Cambridge University Press, London
Daniel, W W (1995) Biostatistics: A foundation for Analyses in the Health Sciences 6th. Wiley, New York
Mead, R; Curnow, R H & Hasted, A M (1993) Statistical Methods in Agriculture and Experimental Biology 2nd. Chapman and Hall, London
Gill, L (1978) Design and analysis of experiments in the animal and medical sciences 3 vols. State University Press, Iowa


This module is at CQFW Level 7