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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)
- Preparation for credit
- 28 hours per semester
- Preparation for classes
- 28 hours per semester
- Class attendance
- 31.5 hours per semester
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Learning outcomes
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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
The prerequisite is the active participation of students in the exercises. 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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