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Biological Data Analysis
Last Updated: 2026-02-05 15:23:44
Abstract
Methods for the analysis of biological data (analysis of variance, linear and generalized linear models, randomisation) are introduced in an applied context. Participants learn to choose appropriate methods for particular research questions, to handle data sets and to analyse them using the software R, to interpret the results, and to represent them in tables and graphs.
Objective
Participants will gain the ability to analyse typical data from biological research using the free statistical software R. Specifically, they will: - get familiar with statistical methods commonly used for the analysis of biological data - know what type of questions and what type of data can be analysed with these methods, as well as conditions for their correct application. - practise data handling and statistical analysis with R - understand the meaning of the results - be able to draw high-quality graphs with R
Content
The course deals with the application of statistical methods in biological research: - What is the purpose of the method? - What type of research questions and what type of data can be analysed with it? - What conditions have to be satisfied? - How can the analyses be done using the sofware R? - What are good representations of the data or results, and how are they obtained with R? - What do the results mean statistically and biologically? The course focuses on the practice of data analsis and NOT on the theoretical background. Participants are strongly advised to complement their training with one or several lecture courses offered by the seminar for statistics ( www.stat.math.ethz.ch ) to obtain a deeper insight in statistical thinking and statistical procedures. Participants are expected to have a basic statistical knowledge (data distributions, descriptive statistics, principles of statistical testing). On request, those who need to acquire or refresh this knowledge will receive material for self-study before the start of the course. No previous experience using R is required; the schedule and content of the first day will be adjusted as much as possible to the participants' experience.
Resources
Lecture Notes
Teaching materials, data sets and help files as well as additional reading will be provided before and during the course in electronic form.
Literature
Sokal, RR & Rohl, FJ. 1995. Biometry. The Principles and Practice of Statistics in Biological Research. 3rd ed. Palgrave Macmillan Freeman ISBN 0-7167-2411-1 77.90 EUR Crawley, MJ. 2007. Statistics. An Introduction using R. Wiley & Sons ISBN 0-470-02298-1 61.00 sFr Van Belle G., Fisher L.D., Heagerty P.J. & Lumley T. 2004. Biostatistics. A Methodology for the Health Sciences, 2nd. ed. Wiley, Hoboken, NJ. Agresti A. 2002. Categorical Data Analysis, 2nd ed. Wiley, Hoboken, NJ.Köhler, W; Schachtel, G & Voleske, P. 2007. Biostatistik. Eine Einführung für Biologen und Agrarwissenschaftler. Springer, Berlin. ISBN 3-540-37710-7, 38.50 sFr.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Biological Data Analysis
Täglich (05.01. bis 17.01.2009), 09:00 bis 15:00 Uhr, Raum LFO G25
|
|
2 h weekly |