300141 VU Multivariate statistical methods in ecology (2024S)
data analysis and modelling
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 08.02.2024 14:00 to Th 22.02.2024 18:00
- Deregistration possible until Fr 15.03.2024 18:00
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
The preliminary meeting will take place via Zoom, on March 13, 0930. Please register for the course, you will receive an invitation to join the preliminary meeting on Monday, March 11.
The course is scheduled as one block, from April 22-26, daily.
Monday
22.04.
09:45 - 18:15
Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
Tuesday
23.04.
09:45 - 13:00
Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
Tuesday
23.04.
13:15 - 18:15
Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
Wednesday
24.04.
09:45 - 18:15
Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
Thursday
25.04.
09:45 - 18:15
Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
Friday
26.04.
09:45 - 18:15
Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
Information
Aims, contents and method of the course
Assessment and permitted materials
Presence throughout the course is compulsory.A written exam of 2 h length, comprising a theoretical and a practical part, will be held at the end of the class. A second and a third opportunity will be offered in accordance with the participants
Minimum requirements and assessment criteria
Successful participants will gain a working understanding of the mathematical and computational mechanics behind the most commonly used statistical methods in ecology. They will understand how to interpret graphical and tabular output from univariate, bivariate and multivariate analyses of ecological datasets as presented in scientific papers and reports. The lecture also explains backgrounds of statistical thinking, hypothesis testing, study planning and experimental design.
Grading will be based on preparation of a course protocol and a written exam testing practical skills for which total of 10 points may be awarded. Failure to produce a coherent and comprehensive course protocol will result in the grade 5, independent of the results of the exam. Up to 3 points may be earned for the protocol, based on coherence, comprehensiveness and quality.
The grading scheme is as follows:
10-9: 1
8-7: 2
6-5: 3
4-3: 4
<3: 5
Points earned for the protocol will be added to points earned during the exam to produce the final score.
Grading will be based on preparation of a course protocol and a written exam testing practical skills for which total of 10 points may be awarded. Failure to produce a coherent and comprehensive course protocol will result in the grade 5, independent of the results of the exam. Up to 3 points may be earned for the protocol, based on coherence, comprehensiveness and quality.
The grading scheme is as follows:
10-9: 1
8-7: 2
6-5: 3
4-3: 4
<3: 5
Points earned for the protocol will be added to points earned during the exam to produce the final score.
Examination topics
Successful participants will be capable of interpreting statistical testing theory, applying and justifying conventions as well as analyzing data sets. They can propose testing schemes for datasets/problems and develop and implement a testing approach based on an example data set.
Reading list
Literature will be provided in form of a course hand-out; additional literature will be presented during the class
Association in the course directory
MEC-5, UF MA BU 01, UF MA BU 04, MNB2, MMEI III
Last modified: Tu 23.04.2024 14:46
We aim at providing an overview (including theoretical background) on statistical testing, in combination with a practical part during which students are to acquire the capacity to independently implement statistical testing strategies.
Lectures on theory and derivation of statistical methods are combined with hands-on R scripting to achieve this goal. Low-level understanding of R is advantageous.