Course Objectives

The students will get knowledge about scientific research methodology and the selection of appropriate statistical methods to manage, compare and present scientific data. The aim is to teach the students critical analysis of information and research results.

The students will learn
· How to design the study/management work for any given situation/case/type of environment
· How to compare and recognize the main parameters of an ecological/environmental event and
· Assessment and delineate environmental management/risk management measures based on scientific reasoning.

Course Description

· Design of field- and experimental work to meet requirements for statistical analyses and scientific relevance
· Descriptive statistics. Sampling area. Sampling methods. Parametric tests and their assumptions: 1-sample and 2-sample t-tests. ANOVA (One and two-factor, multi-factor), multiple comparison tests, interactions, balanced and unbalanced designs, hierarchical analysis of variance, GLM ANOVA.
· Simple and multiple linear regression and correlation. Assumptions of regression analysis. Regression diagnostics. Non-linear regression.
· Non-parametric tests: Sign, Wilcoxon, Mann-Whitney, Kruskal-Wallis, Friedman, Mood's median test. Contingency tables, ?2 tests, Fisher's exact test, log-linear models.
· Multivariate analyses: Principal component analysis, factor analysis, cluster analysis, discriminant analysis, correspondence analysis.

Links:

http://www.biostat.iupui.edu/surf.html

http://www.ec.gc.ca/water/en/info/facts/e_ref.htm

http://www.geus.dk/dvk/i-org-uk.htm

http://www.unicef.org/statistics/index.html

Learning Methods

Lectures.
Seminars.
Case studies.
Interactive.

Assessment Methods

Written examination (60%).
Written essay with oral presentation (40%).

Minor adjustments may occur during the academic year, subject to the decision of the Dean