SOES2036 Quantitative Methods in Marine Science
Module Overview
This module develops analytical skills required for evidence-based interpretation of data generated in the Marine Sciences. The material will focus initially on both the philosophical background of statistical analysis, illustrating how applications in public documents often abuse the basic principles of graphical and statistical analysis. The module will then develop skills in computer literacy and hypothesis testing using marine examples drawn from the spectrum of activities routinely used in final year Honours projects.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Identify statistical procedures appropriate to different types of hypotheses and data.
- Independently use the R environment and know where to search for additional online resources.
- Compute statistical analyses in integrated workflows (reading data in, exporting report and publication-quality figures) using the R environment.
- Present and interpret the results of statistical tests on given data sets
- Contrast your empirical data with the theoretical assumptions to differentiate between the relative support for different conclusions.
Syllabus
This module develops analytical skills required for evidence-based interpretation of data generated in the Marine Sciences. The material will focus initially on both the philosophical background of statistical analysis, illustrating how applications in public documents often abuse the basic principles of graphical and statistical analysis. The module will then develop skills in computer literacy, graphical presentation and hypothesis testing using marine examples drawn from the spectrum of activities routinely used in final year Honours projects.
Learning and Teaching
Teaching and learning methods
The overarching goal is to provide more one-on-one time between educators and students to overcome the initial steep learning curve presenting by statistics and statistical software. • Traditional lectures • Large-group tutorials led by educator based on anonymized student requests for material to revise. • Student-led practical sessions giving hands-on experience of the R environment, • Studenet-led workshops to discuss experimental design in different circumstances. • Short videos of key definitions and concepts via Blackboard • Panopto lecture capture.
| Type | Hours |
|---|---|
| Tutorial | 6 |
| Independent Study | 110 |
| Lecture | 10 |
| Practical classes and workshops | 24 |
| Total study time | 150 |
Resources & Reading list
Textbooks. • Getting Started with R (2017) Beckerman, Childs, Petchy. Oxford University Press • The New Statistics: An Introduction for Biologists (2015) Hector. Oxford University Press
Software. Freely distributed R environment through the more intuitive RStudio interface, both of which are already part of the core University packages.
Assessment
Formative
Class practicals
Summative
| Method | Percentage contribution |
|---|---|
| MCQ-applied knowledge assessment | 70% |
| MCQ-applied knowledge assessment | 30% |
Costs
Costs associated with this module
Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.
In addition to this, students registered for this module typically also have to pay for:
Other
There are no additional costs associated with this module.
Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.