701-1419-00L Analysis of Ecological Data
|Semester||Autumn Semester 2016|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Abstract||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.|
|Objective||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
|Content||- 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
|Lecture notes||Lecture notes and additional reading will be available electronically a few days before the course|
|Literature||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.
|Prerequisites / Notice||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.
- 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
Students without the required knowledge are asked to contact the lecturer before Christmas for support with individual preparation.