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Lecturer(s)
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Mrkvička Tomáš, prof. RNDr. Ph.D.
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Houda Michal, Mgr. Ph.D.
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Rost Michael, doc. Ing. Ph.D.
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Course content
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Lectures: 1 - Introduction, sources of economical data, statistical software used for analysis; 2 - Some aspect of inductive statistical method; 3 - Programming environment R, data import to R; 4 - Two-factor diffusion analysis on how to perform such analysis in the R programming environment; 5 - Nonparametric tests; 6 - Some normality test and how to carry them in R; 7 - Introduction into categorical data analysis; 8 - Visualization of categorical data; 9 - Introduction into multivariate data analysis, matrix algebra, multivariate t-test; 10 - Distance, hierarchical cluster analysis; 11 - Issues of regression analysis, methodology of regression analysis; 12 - Some classification methods; 13 - Logistic regression.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Work with multi-media resources (texts, internet, IT technologies)
- Class attendance
- 16 hours per semester
- Preparation for credit
- 39 hours per semester
- Preparation for classes
- 45 hours per semester
- Preparation for exam
- 40 hours per semester
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Learning outcomes
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The course has two main goals. The first one is to introduce students to some advanced statistical methods like analysis of categorical data, cluster analysis and logistic regression. Introduce terminology linked to these methods and show how to make some conclusion about data. The second goal consists in gaining the basic skill of working with programming environment R. We supposed that students have knowledge of statistic in extent of basic course of statistic.
Students understand the basic principles of advanced statistical methods. Students are able to communicate with programming environment R.
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Prerequisites
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Prerequisities: Teorie pravděpodobnosti a statistika 2/Theory of Probability and Statistics 2
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Assessment methods and criteria
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Combined exam, Test
Examination Requirements: To pass written part of exam you have to solve absolute majority of problems.
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Recommended literature
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Crawley, Michael J. The R book. 2nd ed. Hoboken : Wiley, 2013. ISBN 978-0-470-97392-9.
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De Vries, Andrie; Meys, Joris. R for dummies. 2nd edition. Hoboken, NJ : John Wiley & Sons, Inc., 2015. ISBN 978-1-119-05580-8.
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Hindls, R. a kol. Statistika v ekonomii. Praha: Professional Publishing, 2018. ISBN 978-80-88260-09.
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Meloun,M. Statistická analýza vícerozměrných dat v příkladech. Academia: Praha, 2017.
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