This course has a limited capacity, enrolment is based on application. Application deadline is Thursday in week 33 for the autumn semester. Please see this page for more information: https://www.uib.no/en/matnat/53431/admission-courses-limited-capacity
You will receive confirmation of whether you received a spot in Studentweb no later than Tuesday the week after the deadline.
The time of the first lecture/orientation meeting can be found in the schedule on the course website or on the Mitt UiB learning platform.
It is mandatory to attend the first lecture.
Objectives and Content
The course introduces some statistical tools for analysing and interpreting ecological data. It consists of lectures and computer based practicals covering direct and indirect ordination, cluster analysis and regression trees. The course is followed by a take home exam which covers both theoretical and practical aspects of the course.
After completing the course, students should be able to:
- Explain why the statistical properties of ecological data mean it can needs appropriate methods
- Explain why parsimonious models and methods should be preferred
- Describe the advantages and disadvantages of different numerical methods
- Choose appropriate techniques for analysing data
- Interpret diagnostics
- Build parsimonious models
- Generate and interpret relevant plots
- Analyses data in a modern statistical package
Have some of the statistical skills necessary for their thesis projects
Required Previous Knowledge
Bachelor's degree in Biology, or equivalent.
Recommended Previous Knowledge
Forms of Assessment
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Students will evaluate the course in accordance with the quality assurance system at UiB and the Department. You can find courseevaluations in the Quality Assurance Reports.
Contact the Study Section at the Department of Biological Sciences: email@example.com
Type of assessment: Take-home eksam
- Withdrawal deadline
- Examination system
- Digital exam