Module Information

Module Identifier
Module Title
Academic Year
Semester 2 (Taught over 2 semesters)
Other Staff

Course Delivery

Delivery Type Delivery length / details
Lecture 1 x 2 hour lecture per week
Practical 6 x 2 hour computer workshops in Semester 1


Assessment Type Assessment length / details Proportion
Semester Assessment Continuous assessment  Outcomes assessed: 1, 2, 3, 4  30%
Semester Exam 3 Hours   Open book written examination  Outcomes assessed 1, 2, 3  30%
Semester Assessment Assignment  Outcomes assessed: 1, 2, 3  40%
Supplementary Assessment Data analysis assignment  Outcomes assessed: ALL  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. Assessments associated with the module are compulsory to all students, for Masters' students, attendance at lectures/practicals is optional. ONLY CASIO FX-83 OR FX-85 CALCULATORS MAY BE USED IN THE EXAMINATION

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

Module Skills

Skills Type Skills details
Communication Students are required to present the assignment in an appropriate academic style.
Improving own Learning and Performance Students need to be capable of organising themselves to ensure the assignment is submitted on time.
Information Technology Students will be expected to use statistical packages to analyse data.
Research skills Students are required to collect, analyse and interpret data for the assignment

Reading List

General Text
Campbell, R C (1989) Statistics for Biologists 3rd Cambridge University Press, London Primo search Daniel, W W (1995) Biostatistics: A foundation for Analyses in the Health Sciences 6th edition Wiley, New York Primo search Gill, L (1978) Design and analysis of experiments in the animal and medical sciences Vol 1s. State University Press, Iowa Primo search Gill, L (1978) Design and analysis of experiments in the animal and medical sciences. Vol 2 State University Press, Iowa Primo search Gill, L (1978) Design and analysis of experiments in the animal and medical sciences. Vol 3 State University Press, Iowa Primo search Mead, R; Curnow, R H & Hasted, A M (1993) Statistical Methods in Agriculture and Experimental Biology 2nd edition Chapman and Hall, London Primo search Quinn, B P and Feough, M T (2002) Experimental design and data analysis for biologists Cambridge University Press Primo search


This module is at CQFW Level 7