Module Information
Module Identifier
MAM5120
Module Title
Statistical Concepts, Methods and Tools
Academic Year
2014/2015
Co-ordinator
Semester
Semester 1
Other Staff
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Lecture | 12 x 1 hour lectures |
| Seminars / Tutorials | 12 x 1 hour seminars |
| Practical | 12 x 2 hour practicals |
| Workload Breakdown | Lectures 12 hours Practicals 24 hours Seminars 12 hours Preparation for seminars 24 hours Examination 4 hours Preparation of presentation 6 hours Keeping practical diary 24 hours Self study 94 hours |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | 4 Hours 4-hour practical examination (open book, computer-based) - 50% Computer Room HO C66 if possible Two assessed reports of statistical analyses - 2 x 20% Presentation of results of one analysis - 10% | 50% |
| Supplementary Assessment | Resubmission of failed components |
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
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
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