300141 VU Multivariate statistical methods in ecology (2025S)
data analysis and modelling
Prüfungsimmanente Lehrveranstaltung
Labels
Dieser Kurs gehört im Modul MNB2 zum Bereich Datenanalyse und -modellierung.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Do 06.02.2025 14:00 bis Do 20.02.2025 18:00
- Abmeldung bis Sa 15.03.2025 18:00
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Die Übung wird im Zeitraum vom 5. bis 9. Mai stattfinden. Die Vorbesprechung zum Praktikum findet am 5. März um 13:00h im Seminarraum 3.1, 3.Stock, UBB statt.
- Montag 05.05. 09:45 - 17:30 Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
- Dienstag 06.05. 09:45 - 17:30 Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
- Mittwoch 07.05. 09:45 - 17:30 Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
- Donnerstag 08.05. 09:45 - 17:30 Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
- Freitag 09.05. 09:45 - 17:30 Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Presence throughout the course is compulsory.Preparation of a course protocol is compulsory.A written exam of 2 h length comprising a practical part, will be held.
Mindestanforderungen und Beurteilungsmaßstab
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.
Prüfungsstoff
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.
Literatur
Literature will be provided in form of a course hand-out; additional literature will be presented during the class
Zuordnung im Vorlesungsverzeichnis
MEC-5, UF MA BU 01, UF MA BU 04, MNB2, MMEI III
Letzte Änderung: Mo 04.08.2025 15:26
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.