812352 Statistical analyses of ecological data (in Eng.)
Diese Seite ist erreichbar unter:
- Vorlesung und Übung
- Vortragende/r (Mitwirkende/r)
- Kollmann, Marlene , Rauch, Pablo , Melcher, Andreas
- Angeboten im Semester
- Wintersemester 2020/21
- Unterrichts-/ Lehrsprachen
Aquatic ecosystems and its management require computer supported multivariate analyses to understand their complex functioning. Questions to be answered derive from different spatial and temporal levels. Integrated multivariate models are required to balance different user’s interests.
The understanding of aquatic ecosystems and their biological quality elements like fish needs theoretical and practical statistical knowledge of fundamental and special methods like correlation or regression analyses, principal component analyses, cluster and classification techniques using mainly MS Excel and IBM SPSS. Differnces to R software will be discussed.
The course is structured into 8 statistical units, each has 1 theoretical and 2 practical part. Altogether, presentations, performance, homework and the final written exam, will be rated.
For your practical training, please bring your own computer - MS Office version 2013 - and if possible SPSS 21.
1.Introduction applied limnology data analyses and experimental design
2.Statistical software comparison MS Excel, IBM SPSS, R
3.Descriptive statistics, frequency analyses
4.Tables, figures and graphical inspection
5.Comparison of means
6.Correlation (principal component analyses)
7.Regression (linear, logistic, GLM)
8.Classification (cluster, discriminant function analysis, tree)
- Inhaltliche Voraussetzungen (erwartete Kenntnisse)
The specificity of the course requires basic knowledge in statistics and aquatic ecology.
The objectives of the courses are to give an overview of applied statistics in aquatic ecology and to understand the scientific background and assumptions.
After successful completion of this module, participants are able to:
-Demonstrate ability to select appropriate methodologies for data analysis, based on the specific properties of particular data sets;
-Formulate statistical hypotheses;
-Understand differences of parametric and non-parametric analyses;
-Discuss and to compare means, to calculate correlation and regression coefficients;
-Use different software for data management and data analyses (MS Excel, SPSS);
-Present statistical analyses in tables and graphs.
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