On completion of the course, the student should be able to:
identify statistical methods that can be used to handle large amounts of data
use statistical tools for modelling and analysis within the environmental sciences
carry out a time-series analyses
compute design floods and hydrological extremes
Shapes of probability density functions including measures of central tendency, dispersion and symmetry. Expectation and estimation. Frequency analysis and design floods. Discrete and continuous probability distributions, especially normal and extreme-value distributions. Confidence intervals and hypothesis testing. Correlation, simple and multiple regression. Variance analysis. Parameter-value estimation. Time-series analysis and simple stochastic models. Error theory.
Lectures, computer exercises, project work.
Grading is based on a written examination (4 credits) and exercises and also the written and oral presentation of project reports (1 credit).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.