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Lecturer(s)
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Tuček Pavel, doc. Mgr. Ph.D.
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Marek Jaroslav, Mgr. Ph.D.
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Course content
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1. Repetition and deepening of knowledge from probability theory. Kolmogorov probability space. Random variable. Definition of probability. Distribution functions of a random variable and distribution functions of a random vector. The most important distributions.. 2. Descriptive statistics. One-dimensional statistical data. Polygons and graphs of frequencies, Galton's ogiva. Construction of histogram, boxplot, 3. Types of variables. Method of data collection. Transformation. Statistical characteristics of a random variable. Empirical distribution function. 4. Two-dimensional statistical data. Frequency table. Correlation rates. Variation matrices. Areas of reliability. 5. Goodness-of-fit tests. Normality tests. Independence testing in PivotTables. Nonparametric tests: sign test, Wilcoxon test, Kruskal-Wallis test, Friedman test. Random walk test. 6. Price indices and consumer basket of goods. Laspayres, Paasche and ideal Fisher index. 7. Time series. Moving averages. Exponential smoothing. Technical indicators. Decomposition into trend, cyclical, seasonal and error components. 8. Linear regression: history and development of algorithms: Boškovič, Lambert and Laplace method, least squares method. 9. Nonlinear regression. Orthogonal regression. Regression models with condition. 10. ANOVA. Design of experiments. 11. Statistical quality control. Eligibility. Acceptance plans. 12. Principal component method. Factor analysis. Discriminant analysis. 13. Cluster analysis. Measures of similarity and measures of distance. Hierarchical and non-hierarchical clustering.
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Learning activities and teaching methods
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unspecified, Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book)
- Home preparation for classes
- 36 hours per semester
- Preparation for an exam
- 40 hours per semester
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Learning outcomes
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The aim of the subject is to supplement, deepen and expand the knowledge from the theory of probability and mathematical statistics obtained in the previous study with an emphasis on their application in practice.
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Prerequisites
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Passing the basic course of probability and statistics with annotation corresponding to the basic course of probability and statistics from IT bachelor study. Knowledge of basic distributions, characteristics of random variable and random vector, point and interval estimates, testing of statistical hypotheses and linear regression are assumed.
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Assessment methods and criteria
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Oral examination, Written examination, Home assignment evaluation, Student performance assessment
Obtaining credit from the subject is conditional upon successfully passing a written test - at least 50% of success and writing a semester work.
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Recommended literature
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Anděl, J. Statistické metody.. Praha, 1998. ISBN 8085863278.
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Anděl, J. Základy matematické statistiky.. Praha: Matfyzpress, 2011. ISBN 9788073781620.
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Anderson, T.W. An introduction to multivariate statistical analysis. Hoboken, New York: Wiley-Interscience, 2003. ISBN 9780471360919.
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Antoch, J. a D. Vorlíčková. Vybrané metody statistické analýzy dat. Praha: Academia, 1992. ISBN 8020002049.
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Budíková, M., T. Lerch a Š. Mikoláš. Základní statistické metody. Brno: MU, 2005. ISBN 8021038861.
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Hsu, H.P. Schaum's Outline of Probability, Random Variables, and Random Processes. New York: Mcgraw-Hill, 2012. ISBN 9780071795531.
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Kubanová, J. Statistické metody pro ekonomickou a technickou praxi. Bratislava: Statis, 2008. ISBN 9788085659474.
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Wimmer, G., R. Palenčár a V.Witkovský. Spracovanie a vyhodnocovanie meraní. Bratislava: Veda, 2002. ISBN 9788022407348.
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