Undergraduate

modules

Modules

BIO8052 : Quantitative Methods

Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

The aim of this module is to give the students a solid grounding in key quantitative techniques. Students on entry are likely to have a wide range of abilities in this area, and a primary aim of the module is to ensure that they have all reached a minimum high standard, that can then be built on in other modules later in their degree.

This module is designed to ensure that all students are confident and competent in their use of standard data analytical techniques before they commence their degree. There are no pre-requisites, and the emphasis is to teach methods from the ground-up so that students understand the underlying concepts, and thereby become more confident when using the techniques in statistical software. The module will introduce the theory and practice of data exploration, linear methods, generalised linear methods, and multivariate techniques. They will be introduced to the R statistical modelling package, learn how to summarise data in graphical and tabular format, and show how they can programme the package if necessary for more complex analyses. The package is freely available as open source software, with many good textbooks and web support, and students can install it free of charge on their own PCs if they wish.

Outline Of Syllabus

Recording and simple manipulation of data – numbers of samples, means, SD, CI etc.

Experimental design – Concentrate on difference between variables that are being manipulated, and those that are (potentially) responding to this manipulation.

Presentation – Tabular: means, SD, CI significant digits etc. plus legends and labelling

Presentation – Histograms, multiple comparisons etc.

Presentation – Scatterplots, fitted lines etc.

Continuous response and categorical explanatory, including multiple comparisons not multiple tests

Continuous response and continuous explanatory

Multiple explanatory variables

Categorical or binary response data

Handling multiple response data

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture121:0012:00N/A
Guided Independent StudyAssessment preparation and completion61:006:00Preparation for Blackboard assessment
Guided Independent StudyAssessment preparation and completion12:002:00Blackboard assessment
Guided Independent StudyAssessment preparation and completion110:0010:001000 word report
Guided Independent StudyDirected research and reading121:0012:00Directed reading
Scheduled Learning And Teaching ActivitiesPractical62:0012:00N/A
Guided Independent StudySkills practice62:0012:00Practical prep and follow-up
Guided Independent StudyIndependent study121:0012:00Lecture follow-up
Guided Independent StudyIndependent study122:0022:00N/A
Total100:00
Teaching Rationale And Relationship

Lectures are to introduce key ideas and concepts; practicals to provide hands-on usage of techniques, and possibly introduce more advanced methods.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Computer assessment1M20Blackboard test
Practical/lab report1M80Data interpretation assignment
Assessment Rationale And Relationship

The data used in the assessments will test students’ ability to select, use and interpret a range of appropriate analytical techniques.

Study Abroad students: as the modules are block taught study abroad students should discuss assessment requirements with the module leader.

Reading Lists

Timetable