BIO8052 : Quantitative Methods
- Offered for Year: 2017/18
- Module Leader(s): Dr Roy Sanderson
- Owning School: Natural and Environmental Sciences
- Teaching Location: Newcastle City Campus
|Semester 1 Credit Value:||10|
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
|Scheduled Learning And Teaching Activities||Lecture||12||1:00||12:00||N/A|
|Guided Independent Study||Assessment preparation and completion||6||1:00||6:00||Preparation for Blackboard assessment|
|Guided Independent Study||Assessment preparation and completion||1||2:00||2:00||Blackboard assessment|
|Guided Independent Study||Assessment preparation and completion||1||10:00||10:00||1000 word report|
|Guided Independent Study||Directed research and reading||12||1:00||12:00||Directed reading|
|Scheduled Learning And Teaching Activities||Practical||6||2:00||12:00||N/A|
|Guided Independent Study||Skills practice||6||2:00||12:00||Practical prep and follow-up|
|Guided Independent Study||Independent study||12||1:00||12:00||Lecture follow-up|
|Guided Independent Study||Independent study||1||22:00||22:00||N/A|
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.
The format of resits will be determined by the Board of Examiners
|Computer assessment||1||M||20||Blackboard test|
|Practical/lab report||1||M||80||Data 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 List Website : rlo.ncl.ac.uk