701-1419-00L Analysis of Ecological Data
Semester | Herbstsemester 2017 |
Dozierende | S. Güsewell |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
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 on Mondays 12:45-15:00 from 25 September until 27 November, with the final exam on Monday 4 December. The last two weeks of the semester are free. 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 the first lecture date for support with individual preparation. |