Module Information

Module Identifier
MAM5120
Module Title
Statistical Concepts, Methods and Tools
Academic Year
2016/2017
Co-ordinator
Semester
Semester 1
External Examiners
  • Dr Theodore Kypraios (Associate Professor - University of Nottingham)
 
Other Staff

Course Delivery

Delivery Type Delivery length / details
Seminar 11 x 1 Hour Seminars
Lecture 11 x 1 Hour Lectures
Practical 11 x 2 Hour Practicals
 

Assessment

Assessment Type Assessment length / details Proportion
Semester Exam 4 Hours   4-hour practical examination (open book, computer-based) - 50% Two assessed reports of statistical analyses - 2 x 20% Presentation of results of one analysis - 10%  Physical Sciences room 1.49 preferred  50%
Supplementary Assessment Resubmission of failed components 
Supplementary Exam 4 Hours   Supplementary Exam  Physical Sciences room 1.49 preferred  100%

Learning Outcomes

On completion of this module, students should be able to.
Select and apply appropriate statistical methods to problems in Computational Biology.

Use a statistical package such as R to analyse Biological data.

Design experiments in a way that facilitates correct use of advanced methods for data analysis.

Interpret and report the results of analyses effectively.

Brief description

This module will provide graduates of Computer Science, Mathematics or Biological Sciences with a foundation in Statistics for Computational Biology.
Statistical concepts and ideas are presented using examples from Computational Biology. Various statistical methods are applied using the statistical package R

Content

1. Data and its presentation
2. Introduction to the statistical package R
3. Probability, conditional probability and Bayes? Theorem
4. Statistical distributions
5. Statistical models, estimation and testing.
6. t-tests and z-tests
7. Good and bad experimental design; sample size.
8. ANOVA and multiple comparisons
9. Simple, multiple and curvilinear regression
10. Chi-squared techniques and contingency tables

Module Skills

Skills Type Skills details
Application of Number Inherent in the study of statistics and statistical methods
Communication Seminars and presentation
Improving own Learning and Performance Self study
Information Technology Mastery of a statistical package such as R
Personal Development and Career planning
Problem solving Inherent to application of statistics to problems in Computational Biology
Research skills Experimental design. Extracting relevant information from published sources
Subject Specific Skills Developing expertise in statistical analysis
Team work Joint work in seminars

Notes

This module is at CQFW Level 7