1. Basic using of programming languuage R - principle of the work in language R, algoritmization of statistical tasks, batch processing of data.
2. Random variables and basic characteristics - Density and cumulative density functions.
3. Parameters estimation - Point estimates, Interval estimates.
4. Tests of statistical hypothesis - Basic parametric and nonparametric tests.
5. Analysis of variance (ANOVA) - one- and multifactorial ANOVA.
6. Hierarchical cluster analysis.
7. Factor analysis - idea of the method, identification of factors.
8. Introduction to time series analysis.
9. Decomposition of time series.
10. Autocorrelation analysis.
11. Spectral anlysis.
12. Box - Jenkins methodology - model identification (AR, MA, ARMA, ARIMA, SARIMA), parameters estimation, model checking.
13. Time series prediction.
14. Presentation of statistical outputs - methodology of statistical outputs presentation, mistakes in presentation of numerical and graphical outputs.
2. Random variables and basic characteristics - Density and cumulative density functions.
3. Parameters estimation - Point estimates, Interval estimates.
4. Tests of statistical hypothesis - Basic parametric and nonparametric tests.
5. Analysis of variance (ANOVA) - one- and multifactorial ANOVA.
6. Hierarchical cluster analysis.
7. Factor analysis - idea of the method, identification of factors.
8. Introduction to time series analysis.
9. Decomposition of time series.
10. Autocorrelation analysis.
11. Spectral anlysis.
12. Box - Jenkins methodology - model identification (AR, MA, ARMA, ARIMA, SARIMA), parameters estimation, model checking.
13. Time series prediction.
14. Presentation of statistical outputs - methodology of statistical outputs presentation, mistakes in presentation of numerical and graphical outputs.