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Lecturer(s)
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Course content
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Lectures in English only. 1. Introduction. Probability experiment, random event, the concept of probability, fundamental laws of probability, conditional probability. 2. Independence of events, Bayes theorem. Definition of random variable. Probability distribution of a random variable. Distribution function. 3. Important discrete distributions (binomial, hypergeometric, Poisson). 4. Important continous distributions (exponential, normal). 5. The central limit theorem. Quantiles. Function of random variable. 6. Random vector. Covariance and correlation. 7. Random sample. Descriptive statistics. Point estimate. Confidency intervals. 8. General procedure for testing hypotheses. Testing a claim about a mean. Tests of variances. 9. Chi-square test of goodness of fit. Contingency tables. 10. Correlation. Tests comparing two parameters. F-distribution. 11. Regression analysis. Coefficient of determination. Multiple regression. 12. Reliability function, failure rate. Weibull distribution. Sum of random variables. Gamma distribution. 13. Uses and abuses of statistics. Review. Conclusion.
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Learning activities and teaching methods
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Lecture with practical applications, Collaborative instruction, Self-study of literature
- Contact hours
- 65 hours per semester
- Preparation for formative assessments (2-20)
- 15 hours per semester
- Preparation for comprehensive test (10-40)
- 26 hours per semester
- Preparation for an examination (30-60)
- 50 hours per semester
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| prerequisite |
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| Knowledge |
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| Students should have a basic knowledge of combinatorics (high school level) and basic knowledge of calculus of one real variable. |
| learning outcomes |
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| On completion of this course the student will be able: - to describe random events and to compute their probabilities - to identify and describe continous or discrete random variable - to recognize basic types of discrete or continous distributions of probability - to use methods of descriptive statistics to summarize data - to enumerate point estimates and construct confidence intervals - to formulate statistical hypothesis and to choose an appropriate statistical test for its accception or rejection - to interpretate statistical results - to select an adequate plan for statistical experiments |
| teaching methods |
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| Collaborative instruction |
| Self-study of literature |
| Interactive lecture |
| assessment methods |
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| Combined exam |
| Test |
| Skills demonstration during practicum |
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Recommended literature
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Bowerman, Bruce L.; O'Connell, Richard T. Applied statistics : improving business processes. Chicago : Irwin, 1997. ISBN 0-256-19386-X.
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Brase, Charles Henry; Brase, Corrinne Pellillo. Understandable statistics : concepts and methods. Lexington : D.C. Heath, 1987. ISBN 0-669-12181-9.
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Grimmett, Geoffrey R.; Stirzaker, David R. Probability and Random processes. Oxford : Oxford University Press, 2001. ISBN 0-19-857222-0.
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Ross, Sheldon. A first course in probability. Prentice-Hall, New York, 2001. ISBN 978-0130338518.
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