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Lecturer(s)
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Rost Michael, doc. Ing. Ph.D.
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Course content
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1 - introduction to the course, organization of study, requirements for the student, historical context, 2 - statistical software for biological data analysis (R, STATISTICA, Excel, and more ...) 3 - introduction to probability, random variable, distribution of random variables 4 - basic data set processing (data sorting, position measures, variability, skewness and sharpness, graphs) 5 - confidence intervals of selected characteristics and their construction 6 - introduction to hypothesis testing 7 - verification of data normality (tests and graphical verification) 8 - selected one-sample parametric tests (one-sample t-test, z-test,) 9 - two-sample parametric tests (t-tests, two-sample F-test, test for equality of relative frequencies) 10 - nonparametric variants of one-sample and two-sample tests 11 - planning of experiments, field experiments, 12 - analysis of variance and related methods (one-factor ANOVA, homoskedasticity tests, K-W test, Friedman's test, selected tests of multiple comparison) 13 - introduction to regression and correlation analysis The content of the exercise blocks - corresponds to the thematic units of the lectures
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing)
- Preparation for exam
- 35 hours per semester
- Preparation for classes
- 20 hours per semester
- Class attendance
- 56 hours per semester
- Preparation for credit
- 35 hours per semester
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Learning outcomes
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The aim of the course is to teach students to process, evaluate and interpret information obtained by measurement or observation through selected basic biostatistical methods applicable in agricultural disciplines.
The student is able to perform basic processing and evaluation of experimentally obtained data through basic statistical methods.
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Prerequisites
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Basic knowledge of mathematics in the scope of grammar school. Work with computer.
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Assessment methods and criteria
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Combined exam
Basic knowledge of mathematics in the scope of grammar school. Work with computer.
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Recommended literature
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Anděl, Jiří. Statistické metody. Vyd. 3. Praha : Matfyzpress, 2003. ISBN 80-86732-08-8..
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Dalgaard, Peter. Introductory statistics with R. New York : Springer-Verlag, 2002. ISBN 0-387-95475-9..
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Hátle, Jaroslav; Kahounová, Jana. Úvod do teorie pravděpodobnosti. 1. vyd. Praha : SNTL, 1987..
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Lepš, Jan; Šmilauer, Petr. Biostatistika. Vydání 1. České Budějovice : Episteme, nakladatelství Jihočeské univerzity v Českých Budějovicích, 2016. ISBN 978-80-7394-587-9..
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Mrkvička, Tomáš; Petrášková, Vladimíra. Úvod do teorie pravděpodobnosti. 1. vydání. České Budějovice : Jihočeská univerzita, 2008. ISBN 978-80-7394-115-4..
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