701-1419-00L Analysis of Ecological Data
Semester | Herbstsemester 2016 |
Dozierende | S. Güsewell |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Lehrveranstaltungen
Nummer | Titel | Umfang | Dozierende | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
701-1419-00 G | Analysis of Ecological Data Block course from 12 to 18 January 2017. | 2 Std. |
| S. Güsewell |
Katalogdaten
Kurzbeschreibung | This class provides students with an overview of techniques for data analysis used in modern ecological research, as well as practical experience in running these analyses with R and interpreting the results. Topics include linear models, generalized linear models, mixed models, model selection and randomization methods. |
Lernziel | Students will be able to: - describe the aims and principles of important techniques for the analysis of ecological data - choose appropriate techniques for given problems and types of data - evaluate assumptions and limitations - implement the analyses in R - represent the relevant results in graphs, tables and text - interpret and evaluate the results in ecological terms |
Inhalt | - Linear models for experimental and observational studies - Model selection - Introduction to likelihood inference and Bayesian statistics - Analysis of counts and proportions (generalised linear models) - Models for non-linear relationships - Grouping and correlation structures (mixed models) - Randomisation methods |
Skript | Lecture notes and additional reading will be available electronically a few days before the course |
Literatur | Suggested books for additional reading (available electronically) Zuur A, Ieno EN & Smith GM (2007) Analysing ecological data. Springer, Berlin. Zuur A, Ieno EN, Walker NJ, Saveliev AA & Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York. Faraway JJ (2006) Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis. |
Voraussetzungen / Besonderes | Time schedule The course takes place over a period of nine days from Thursday 12.01 to Friday 20.01, with classes on 12, 13, 16, 17 and 18.01. and an exam in the morning of 20.01. Prerequisites - Basic statistical training (e.g. Mathematik IV in D-USYS): Data distributions, descriptive statistics, hypothesis testing, linear regression, analysis of variance - Basic experience in data handling and data analysis in R Individual preparation Students without the required knowledge are asked to contact the lecturer before Christmas for support with individual preparation. |
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
ECTS Kreditpunkte | 2 KP |
Prüfende | S. Güsewell |
Form | benotete Semesterleistung |
Prüfungssprache | Englisch |
Repetition | Repetition nur nach erneuter Belegung der Lerneinheit möglich. |
Zusatzinformation zum Prüfungsmodus | The examinations takes place on January 20th, 2017 from 9 to 11 am at CHN F 46. |
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar. | |
Es werden nur die öffentlichen Lernmaterialien aufgeführt. |
Gruppen
Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
Keine zusätzlichen Belegungseinschränkungen vorhanden. |
Angeboten in
Studiengang | Bereich | Typ | |
---|---|---|---|
Biologie Master | Wahlpflicht Masterkurse | W | |
Umweltnaturwissenschaften Master | Fachkenntnisse zu quantitativen und rechnerischen Verfahren | W |