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Lecturer(s)
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Schindler Martin, Mgr. Ph.D.
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Course content
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lectures and practicals: 1. Environment of statistical software R and RStudio. 2. Data import and export, data manipulation. 3. Syntax, programming in R. 4. Functions, loops, conditional statements. 5. Summaries and graphical outputs in R, RMarkdown. 6. Basic data structures and work with them. 7. Data manipulation, transformation and selection. 8. Descriptive statistics of one-dimensional data, boxplot, histogram. 9. Descriptive statistics of multidimensional data; scatterplot diagram, correlation. 10. Basics of probability distributions, generating of random numbers. 11. Calculation and graphical display of confidence intervals. 12. Hypotheses testing of location parameter, t-tests and their nonparametric alternatives. 13. Analysis of variance, verification of the assumptions. 14. Regression models, regression diagnostics.
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing)
- Class attendance
- 42 hours per semester
- Preparation for exam
- 106 hours per semester
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Learning outcomes
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Students will familiarize with the basic concepts of the statistical software R and its extensions in order to learn the basic programming technics and basics of statistical analysis in R.
Basic knowledge of programming technics and statistical analysis in R.
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Prerequisites
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Elements of probability theory, data analysis and statistics.
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Assessment methods and criteria
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Oral exam, Written exam
work at the practicals, semestral work/final test
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Recommended literature
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Anděl, J. Statistické metody. Matfyzpress: Praha, 2007. ISBN 978-80-7378-003-6.
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Dalgaard, P. Introductory Statistics with R. 2008. ISBN 978-0-387-79053-4.
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