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Lecturer(s)
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Tellbüscher Anil Axel, MSc.
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Blažek Petr, RNDr. Ph.D.
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Course content
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Content of lectures: Introduction to statistics Basic descriptive statistics Probability and likelihood Testing of hypotheses Goodness-of-fit test, contingency tables t-tests and their non-parametric counterparts Analysis of variance and its non-parametric counterparts Correlation and linear regression Multiple regression and general linear models Non-linear regression Introduction to multivariate methods Content of practicals: Practicals are focused on computing of statistical methods theoretical principles of which are covered by the lectures. In addition, graphical presentation of data and analysis results is emphasized. All computations are carried out in R.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Work with multi-media resources (texts, internet, IT technologies)
- Preparation for classes
- 20 hours per semester
- Preparation for exam
- 20 hours per semester
- Preparation for credit
- 20 hours per semester
- Class attendance
- 52 hours per semester
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Learning outcomes
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The aim of the course is to introduce the principles of statistical thinking and the use of statistics in science. In addition to this theoretical background, special emphasis is put on practical use of statistical analyses for data processing. After completing the course, the students should be able to process their own data and apply basic statistical methods to test hypotheses related to topics of their bachelor theses.
Student can think in the context of statistical principles (experimental design, logic of scientific work), knows basic statistical methods (see Content), understands their results when reading publications, and is able to perform them independently in R language.
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Prerequisites
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Basic knowledge of math and MS Excel at the high-shool level.
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Assessment methods and criteria
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Written examination, Essay, Interim evaluation
The evaluation consists of three parts: - mid-term test (20 points) - essay (20 points) - final test (60 points) Points from these three parts are summed up, a minimum of 50 points is required to pass the course. Submission of an essay meeting basic requirements is considered to be the course credit, without which it is not possible to pass the course. The tests are open-textbook exams, focused on the basic theory and on the practical use of statistical methods using R. The essay should resemble very brief scientific publication / bachelor thesis, where students use some methods they learned in this course. Details are published on the course webpage (see Content).
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Recommended literature
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Crawley M.(2007): The R Book. John Wiley & Sons Ltd, Chichester..
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Beckerman A.P., Petchey O.L. Getting started with R. An introduction for biologists.. Oxford University Press, Oxford, 2012. ISBN 987-0-19-960162.
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Lepš J., Šmilauer P. Biostatistics with R. Cambridge, 2020.
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Paradis E. R for Beginners. Montpellier, 2005.
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Yakir B. Introduction to Statistical Thinking (With R, Without Calculus). The Hebrew University, Jerusalem, 2011.
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