300368 UE Practical data analysis in ecology, biodiversity and zoology (2025S)
Data Analysis and modelling
Prüfungsimmanente Lehrveranstaltung
Labels
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. 22 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Der erste Termin (05.03.2025) ist eine Vorbesprechung und dient zur endgültigen Klärung der Teilnehmerliste sowie für organisatorische Fragen. Teilnahme verpflichtend!
- Mittwoch 05.03. 15:00 - 16:00 Seminarraum (Fakultätszentrum für Biodiversität) Rennweg EG
- N Montag 01.09. 13:00 - 17:00 Seminarraum (Fakultätszentrum für Biodiversität) Rennweg EG
- Dienstag 02.09. 13:00 - 17:00 Seminarraum (Fakultätszentrum für Biodiversität) Rennweg EG
- Mittwoch 03.09. 13:00 - 17:00 Seminarraum (Fakultätszentrum für Biodiversität) Rennweg EG
- Donnerstag 04.09. 13:00 - 17:00 Seminarraum (Fakultätszentrum für Biodiversität) Rennweg EG
- Freitag 05.09. 13:00 - 17:00 Seminarraum (Fakultätszentrum für Biodiversität) Rennweg EG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Some basic theory, but mostly practical application, of a range of statistical tests and methods that are frequently used in ecology, zoology and biodiversity research. Participants are required to work on their own computer!This course aims at familiarizing MSc students with an interest in organismal biology with a range of fequentist (i.e. non-Bayesian) statistical concepts and tools. Methods covered include linear models, correlation and regression, analysis of contingency tables, constrained and unconstrained ordinations, some non-parametric alternatives to classic linear models, and species diversity statistics. The focus is on practical solution of common problems, using freely available software packages (e.g. PAST, JASP), and on acquiring expertise which methods to be used with what types of data, but without the need to dwell into programming codes like in the widely used R or Python languages.
Art der Leistungskontrolle und erlaubte Hilfsmittel
At the end of the course, a set of statistical problems will be distributed. These have to be elaborated as a home work individually. One single pdf file containing the solutions (statistical results, graphics) has to be submitted electronically, at latest by 01 October 2025. Participants are allowed to use any means of support for solving these problems, as long as they properly cite all materials used.
Mindestanforderungen und Beurteilungsmaßstab
Some prior basic knowledge of statistical principles and methods is helpful.
To obtain a positive grade, submission of the 'homework' at the end of the course is mandatory. In this homework, 50 % of the points available need to be reached for a positive grading. 50-62.5%: grade 4; 62.5-75%: grade 3; 75-87.5%: grade 2; >87.5%: grade 1.
To obtain a positive grade, submission of the 'homework' at the end of the course is mandatory. In this homework, 50 % of the points available need to be reached for a positive grading. 50-62.5%: grade 4; 62.5-75%: grade 3; 75-87.5%: grade 2; >87.5%: grade 1.
Prüfungsstoff
For the final 'homework', participants will work on model data sets provided, using the methods presented and applied during the course week.
Literatur
Some hints will be offered in the initial meeting and later during the course week in September. Any standard text book on statistics may be used to update the participants' knowledge, if required.
Zuordnung im Vorlesungsverzeichnis
MEC-5, MZO3, MNB2
Letzte Änderung: Mi 05.03.2025 10:27