Total Credits: 10
Level: Level 4
Target Students: Students registers for MSci degrees within the School of Biology.
|Autumn||Assessed by end of Autumn Semester|
Prerequisites: Registered for a degree in the School of Biology. Only available to Year 4 students.
Summary of Content: An advanced level biological statistics course, building on basic undergraduate (Levels 1 and 2) training. Lectures discuss concepts in experimental design, biological probability, generalised linear modelling and multivariate statistics. Practical sessions build on this conceptual outline, giving hands on experience of problem solving and analytical software, and some basic programming skills.
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||11 weeks||1 per week||2 hours|
|Workshop||4 weeks||1 per week||1 hour|
Method of Assessment:
|Coursework 1||25||Answers to workshop problems|
|Coursework 2||25||Answers to workshop problems|
|Coursework 4||25||Answers to workshop problems|
|Inclass Exam 1 (Written)||25||Written exam - 1 hour|
Dr T Reader
Education Aims: Building on basic undergraduate training, this module will outline a range of statistical techniques that students are likely to encounter during their research projects. We will also discuss the most common experimental design problems faced by biologists. The objective is not to give detailed training in all techniques, but to provide a conceptual toolkit enabling students to develop solutions to a range of problems, and a basis from which to explore the relevant literature and software in their own time.
Learning Outcomes: Students will: A2. Learn about current trends and developments in approaches to the design of experiments and the analysis of complex datasets in biology. A4. Learn to use appropriate terminology in statistics and experimental design when talking about their work. B1. Critically analyse and interpret published information and data. B3. Understand classical and complex problems in experimental designs and learn to recognise them in real biological scenarios. C1. Tackle research questions using quantitative analysis of data. C4. Undertake appropriate experimental design and statistical analysis. D4. Use and access information technology, including statistical packages and a programming language. D6. Manage and manipulate numerical data.
Offering School: Life Sciences
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