Lecturer(s)


Schindler Martin, Mgr. Ph.D.

Course content

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 onedimensional 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, ttests and their nonparametric alternatives. 13. Analysis of variance, verification of the assumptions. 14. Regression models, regression diagnostics.

Learning activities and teaching methods

Monological explanation (lecture, presentation,briefing)
 Class attendance
 42 hours per semester
 Preparation for exam
 106 hours per semester

Learning outcomes

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.

Prerequisites

Elements of probability theory, data analysis and statistics.

Assessment methods and criteria

Oral exam, Written exam
work at the practicals, semestral work/final test

Recommended literature


Anděl, J. Statistické metody. Matfyzpress: Praha, 2007. ISBN 9788073780036.

Dalgaard, P. Introductory Statistics with R. 2008. ISBN 9780387790534.
