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Lecturer(s)
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Marek Jaroslav, Mgr. Ph.D.
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Course content
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Measurement and integral basics. Definition of the probability. Independent events. Random variables. Random vectors. Common probability distributions. Independent random variables. Limit theorems. Parameters estimation. Confidence intervals for expected value and for variance. Hypothesis testing. Regression analysis. Correlation analysis. Analysis of Variance.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing)
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Learning outcomes
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The aim of the course is to acquaint the students with theory of probability and statistical principles in order to apply it in real situations.
Student will be able to use statistical methods and theory of probability in real situations.
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Prerequisites
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Good mathematical skills. Good integral and differential calculus knowledge.
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Assessment methods and criteria
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Oral examination, Written examination
The credit is granted upon completion of following conditions: active participation in seminars (labs); max. 3 absences. The examination comprises of three parts: written theory and practical tests; at least 51% success rate in each part is required; successful speaking test.
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Recommended literature
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Fahrmeir und koll. Statistik. Springer - Verlag. Berlin, 2004.
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Kubanová, J., Linda, B. Sbírka příkladů z pravděpodobnosti. Statis, 2004.
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Kubanová, J. Statistické metody pro ekonomickou a technickou praxi. Statis, 2004.
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Sirvastava, M., S. Methods of multoivariate statistics. Wiley, New York, 2002.
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