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Lecturer(s)
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Course content
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Scope: Introduction to statistics, basic descriptive statistics (position, variability, precision), probability distributions. Hypothesis testing, goodness-of-fit test, contingency tables, t-test, analysis of variance, correlation, linear and non-linear regression, general linear models. Data transformations, rank based tests, permutation tests, brief mention of advanced methods (mixed effect models, generalized linear models, multivariate methods). Tutorials: Tutorials introduce work with R software using RStudio, and show practical application of statistical methods introduced in the lectures. Interpretation of the results and their graphical presentation is emphasized.
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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 time limit is 45 minutes. The essay should resemble very brief scientific publication / bachelor thesis, where students use some methods they learned in this course. Details are published in the e-learning page.
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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: Oxford University Press, 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). Jerusalem: The Hebrew University, 2011.
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