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.

Aims
This module aims to provide the student with:

An introduction to statistical ideas and logic involved in making inferences from experimental data. This will be applied to give an understanding of how to use a statistical computing package.

Learning outcomes
On completion of this module students will be able to:

• identify appropriate methods of analysis for different types of data.

• analyse data using a range of statistical analyses.

• interprete the results of data analyses.

• apply statistical knowledge in the content of postgraduate reserch.

Syllabus / Curriculum design
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.