| Course Unit Code | 230-0229/01 |
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| Number of ECTS Credits Allocated | 2 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| PAL39 | RNDr. Radomír Paláček, Ph.D. |
| SVE0150 | Ing. Veronika Svetlíková, Ph.D. |
| Summary |
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| Learning Outcomes of the Course Unit |
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| The students should get acquainted with the main concepts of advanced statistical methods. Knowledge of the principles of work with programming language R. On completion of this course students will acquire knowledge of methods of time series analysis, their decomposition, time series forecasting, model identification methodology and spectral analysis. |
| Course Contents |
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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.
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| Recommended or Required Reading |
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| Required Reading: |
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Doležalová, J.-Pavelka, L.: Pravděpodobnost a statistika. Skriptum VŠB, Ostrava 2005. ISBN 80-248-0948-6.
Otipka, P.-Šmajstrla, V.: Pravděpodobnost a statistika. Skriptum VŠB-TU, Ostrava 2006. ISBN 80-248-1194-4. ( http://www.studopory.vsb.cz/studijnimaterialy/past/past.pdf )
http://mdg.vsb.cz/portal/en/Statistics1.pdf |
Doležalová, J.-Pavelka, L.: Pravděpodobnost a statistika. Skriptum VŠB, Ostrava 2005. ISBN 80-248-0948-6.
Otipka, P.-Šmajstrla, V.: Pravděpodobnost a statistika. Skriptum VŠB-TU, Ostrava 2006. ISBN 80-248-1194-4. ( http://www.studopory.vsb.cz/studijnimaterialy/past/past.pdf )
http://mdg.vsb.cz/portal/m3/index.php |
| Recommended Reading: |
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Meloun, M. -- Militký, J. Kompendium statistického zpracování dat: metody a řešené úlohy, Praha, Academia, 2006, 80-200-1396-2
Andrew C. Harvey. Time Series Models. Harvester wheatsheaf, 1993. |
Meloun, M. -- Militký, J. Kompendium statistického zpracování dat: metody a řešené úlohy, Praha, Academia, 2006, 80-200-1396-2
Andrew C. Harvey. Time Series Models. Harvester wheatsheaf, 1993. |
| Planned learning activities and teaching methods |
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| Individual consultations, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |