Lecturer(s)
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Mrkvička Tomáš, prof. RNDr. Ph.D.
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Course content
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Contents of lectures: 1. Random events, combinatorics. 2. Classical definition and geometrical definition of probability. Examples. 3. Definition of probability, probability of intersection, union. 4. Dependent and independent random events. 5. Conditional probability. 6. Discrete random variables. Distribution function. 7. Alternative, binomial, Poisson, geometrical distributions. 8. Continuous random variables. 9. Uniform, exponential normal distributions. 10. Descriptive statistics. 11. Student, Chi2, F - distribution. Quantiles. 12. Hypothesis testing. One sample t-test. 13. T-tests, paired and two-sample.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming)
- Class attendance
- 14 hours per semester
- Preparation for classes
- 35 hours per semester
- Preparation for credit
- 15 hours per semester
- Preparation for exam
- 20 hours per semester
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Learning outcomes
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A basic introductory course devoted to basics of theory of probability.
Students understand basic principals of probability and descriptive statistics
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Prerequisites
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Prerequisities: KMI/MATI or KMI/MATIA Mathematics 1
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Assessment methods and criteria
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Combined exam
Credit requirements: 1) in the first term: successful completion of two intermediate credit tests, with at least 55% on average for both intermediate tests combined, 2) in the second term: successful completion of the remedial test with a pass rate of at least 55%.
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Recommended literature
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Freeman, J., Shoesmith, E., Sweeney, D., Anderson, D., Williams, T. Statistics for Business and Economics. Cengage, 2017.
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NEWBOLD, P., CARLSON, W, L., THORNE, B. Statistics for Business and Economics. Upper Saddle River : Pearson Prentice Hall, c2010, 2010. ISBN 978-0-13-507248.
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