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Lecturer(s)
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Fiala Jan, RNDr. et PhDr. Ph.D.
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Houda Michal, Mgr. Ph.D.
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Berková Ilona, Ing. Ph.D.
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Course content
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1. Introduction, Content and aim of the course. Additional examples working with random event. 2 - 3. Some probability examples: basic probability concept, conditional probability. 4 - 5. Discrete random variable, distribution function, expected value and variance. 6 - 7. Continuous random variable, density, distribution function, expected value and variance. 8 -9. Descriptive statistics, graphical tools, Descriptive statistics, for continuous, nominal and ordinal data. 10. Quantiles of various distributions of random variables 11. One sample t-test and his alternatives, practical examples 12-13. Two sample t-test and his alternatives, practical examples
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Learning activities and teaching methods
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Dialogic (discussion, interview, brainstorming), Demonstration, Laboratory
- Class attendance
- 21 hours per semester
- Preparation for classes
- 21 hours per semester
- Preparation for credit
- 14 hours per semester
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Learning outcomes
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The aim of course is to introduce students to the theory of probability.
Students understand the basic principles of probability theory and descriptive statistics.
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Prerequisites
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The course has no prerequisities.
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Assessment methods and criteria
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Test
Credit Requirements: Homeworks. Passing two tests.
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Recommended literature
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Anděl, J. Statistické metody. Praha: Matfyzpress, 2007.
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Florescu, I., Tudor, C. Handbook of probability. New Jersey: John Wiley &Sons, 2014. ISBN 978-0-470-64727-1.
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Likeš, J., Machek, J. Počet pravděpodobnosti. SNTL, Praha, 1987.
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