Data analysis in environmental science

Type of instruction






Recommended in

Semesters 1-4

Typically offered in

Autumn/Spring semester

Course description

Goals of the course:
• To give an introduction into R, a freely available statistical and computational environment, which is widely used by scientists all over the world.
• To change the perspective of the students on statistics. In particular that data are realizations of random variables and thus descriptive statistics, which depend on the data, are also random variables.
Introduction to R, in particular its basic commands, possibilities for reading in data, storing and preprocessing them, basics of graphical tools. The subsequent goal is to introduce and apply univariate data analysis methods in R covering the following topics:
- statistical sample, basic statistics
-requirements of estimation
-histograms, distributions and their assessment
- box-and-whiskers plots
- basics of statistical tests
- chi square test and its applications: fitting, homogeneity and independency analyses
- correlation coefficients and correlation matrices
- basic notions of regression analysis, estimation of linear regression from the sample, estimation error, nonlinear regression

  • W. John Braun and Duncan J. Murdoch: A First Course in Statistical Programming with R, 2007, Cambridge University Press, ISBN-13 978-0-521-87265-2

  • Yosef Cohen, Jeremiah Y. Cohen: Statistics and Data with R: An applied approach through examples, 2008, John Wiley & Sons Ltd, ISBN 978-0-470-75805-2

  • Michael J. Crawley: The R Book, 2007, John Wiley & Sons Ltd, ISBN-13: 978-0-470-51024-7