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Lecturer(s)
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Zemek František, doc. Mgr. Ing. Ph.D.
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Bystřický Václav, Ing. Ph.D.
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Course content
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Lectures: 1) Introduction to the course, requirements, basic terminology; 2) Probability; 3) Random variable; 4) Probability distribution 5) Descriptive statistics; 6) Estimates and Confidence Intervals; 7) Introduction to hypothesis testing; 8) Single-line t-test; 9) Two-choice t-test and pair t-test; 10) One-way ANOVA; 11) Multiple comparison; 12) Nonparametric methods; 13) Regression and correlation analysis;
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming)
- Preparation for classes
- 50 hours per semester
- Class attendance
- 16 hours per semester
- Preparation for exam
- 100 hours per semester
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Learning outcomes
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The course introduces the basic procedures for data processing. Introduces students to the probability calculus, deals with system descriptors and basic principles of inductive reasoning.
Students understand basic principles of statistical methods and probability. They are able to perform hypothesis testing and regression analysis.
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Prerequisites
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knowledge of secondary school mathematics
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Assessment methods and criteria
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Combined exam, Test
Credit requirements: Active participation in lessons. Exam requirements: To successfully complete the written part of the exam, it is necessary to obtain min. 60% points. It applies to both the computer and theory test. Successful completion of the computer test is a condition for taking the theory test.
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Recommended literature
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Anděl, J. Základy matematické statistiky. Praha : MFF UK, 2005.
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ČERMÁKOVÁ, A. Statistika II. JU v Českých Budějovicích, 2005.
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DRAPER, N., SMITH, H. Applied Regression analysis, Wiley and Sons. New York, 1981.
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HENDL, JAN. Přehled statistických metod zpracování dat : analýza a metaanalýza dat. Praha, Portál, 2004.
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ZVÁRA, K. Biostatistika. Praha : Karolinum, 1998. ISBN 80-7184-773-9.
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