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Lecturer(s)
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Weidinger Karel, doc. Mgr. Dr.
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Course content
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Practical application of modern methods of data collection, analysis and presentation. Observation vs experiment, correlation vs causality, exploratory vs confirmatory methods. Biological vs statistical significance, effect size vs significance. Design of experiments, replication vs pseudoreplication. General/generalized linear models, types of response and explanatory variables, fixed vs random effects. Significance testing vs model selection. Visualization and presentation of results.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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To learn basic principles of statistical data analysis.
Student should be able to: - analyze data using common statistical methods. - interpret results and draw conclusions. - present results in a scientific publication.
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Prerequisites
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Elementary computer skills.
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Assessment methods and criteria
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Written exam
Active participation in class. Completion of midterm tasks. Completion of final test - practical data analysis problem. Qualified discussion on selected topics.
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Recommended literature
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Manuály a dokumentace statistických programů..
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Grafen A, Hails R. (2002). Modern statistics for the life sciences. Oxford.
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Sokal R, Rohlf FJ. (1995). Biometry.. New York.
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Statsoft Inc. Electronic Statistics Textbook..
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