1. Working with data. Types of data. Data Collection. Primary data. Data transcription and control. EDA. CDA. Data transformation.
2. Ecological data and its use. Ecological similarity. Biodiversity indices. Ellenberg\'s indication values. Traits.
3. Basic terminology of multicriterial statistical methods.
4. Regression. Linear models. Regression curves.
5. Ordination analysis. Models of species response on environment gradient. Basic ordination techniques and methods.
6. Indirect gradient analysis. PCA (Principal Components Analysis). CA (Correspondence Analysis). DCA (Detrended Correspondence Analysis).
7. Direct gradient analysis. RDA (redundancy analysis). CCA (canonical correspondence ananlysis).
8. Null hypothesis. Monte Carlo permutation test. Testing statistics.
9. Case study.
10. Classification methods. Nonhierarchical classification.
11. Classification methods. Hierarchical classification. Divisive classification.
12. Case study.
13. Design of experiments - manipulation vs. natural experiments.
2. Ecological data and its use. Ecological similarity. Biodiversity indices. Ellenberg\'s indication values. Traits.
3. Basic terminology of multicriterial statistical methods.
4. Regression. Linear models. Regression curves.
5. Ordination analysis. Models of species response on environment gradient. Basic ordination techniques and methods.
6. Indirect gradient analysis. PCA (Principal Components Analysis). CA (Correspondence Analysis). DCA (Detrended Correspondence Analysis).
7. Direct gradient analysis. RDA (redundancy analysis). CCA (canonical correspondence ananlysis).
8. Null hypothesis. Monte Carlo permutation test. Testing statistics.
9. Case study.
10. Classification methods. Nonhierarchical classification.
11. Classification methods. Hierarchical classification. Divisive classification.
12. Case study.
13. Design of experiments - manipulation vs. natural experiments.