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Lecturer(s)
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Machalová Jitka, doc. RNDr. Ph.D., MBA
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Course content
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1. Point and interval estimates, principle of hypothesis testing. 2. Statistical analysis of a pair of quantitative and/or qualitative variables. 3. Bayes' Theorem and its application, priors and posteriors, Monte Carlo methods. 4. Classification: classical methods and high dimensional methods. 5. Regression analysis and its application, model construction and verification, correlation analysis. 6. Exploratory statistical analysis, clustering, reduction of dimension. 7. Time series modelling: trends and periodicity, Box-Jenkins approach. 8. Supervised machine learning: neural networks, support vector machines, and decision trees.
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Learning activities and teaching methods
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Work with Text (with Book, Textbook)
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Learning outcomes
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Realize contexture of basic conceptions and statements concerning advanced statistical disciplines.
Synthesis Realize contexture of basic conceptions and statements concerning advanced statistical disciplines.
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Prerequisites
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The student has to meet all prerequisites given for the bachalor tudy course Applied Mathematics and all the conditions of Study and Examination Regulations of the Palacký University in Olomouc.
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Assessment methods and criteria
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Oral exam
the student has to understand the subject
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Recommended literature
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Anděl, J. (2005). Základy matematické statistiky. Praha.
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Bishop, Ch. M. (2011). Pattern recognition and machine learning.
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Everitt, B., Hothorn, T. (2011). An introduction to applied multivariate analysis with R. Springer, Heidelberg.
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Hindls R., Hronová S., Seger J., Fischer J. (2007). Statistika pro ekonomy.
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Hron K., Kunderová P., Vencálek O. (2018). Základy pravděpodobnosti a metod matematické statistiky. Olomouc.
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MacKay D. (2003). Information theory, Inference, and learning algorithms.
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