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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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Contents of blocks of lectures and exercises: 1. Introduction to the course, organization of study, requirements for the student, historical context. 2. Statistical software for analysis of biological data (R, STATISTICA, Excel). 3. Introduction to statistical inference. 4. Basic data set processing (data sorting, position measures, variability measures, 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 experiments, field experiments. 12.-14. Analysis of variance and related methods (one-factor ANOVA, homoskedasticity tests, K-W test, Friedman's test, selected multiple comparison tests).
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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 credit
- 30 hours per semester
- Preparation for classes
- 35 hours per semester
- Class attendance
- 39 hours per semester
- Preparation for exam
- 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.
Students understand the basic principles of statistical methods and probability. They are able to perform basic data processing and test statistical hypotheses.
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Prerequisites
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This subject has no prerequisites.
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Assessment methods and criteria
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Combined exam
Active participation on lessons and exercises, or participation in seminars. Fulfillment of assigned tasks according to the teacher's instructions.
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Recommended literature
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Anděl, Jiří. Statistické metody. 2., přeprac. vyd. Praha : Matfyzpress, 1998. ISBN 80-85863-27-8.
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Lepš, Jan; Šmilauer, Petr. Biostatistika. České Budějovice : Jihočeská univerzita, Přírodovědecká fakulta, 2014.
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Verzani, John. Using R for introductory statistics. Boca Raton : Chapman and Hall/CRC, 2005. ISBN 1-58488-4509.
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Zar, Jerrold H. Biostatistical analysis. 3rd ed. London : Prentice-Hall, 1996. ISBN 0-13-086398-X.
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