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Applied statistics

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code230-0229/01
Number of ECTS Credits Allocated2 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *Second Cycle
Year of Study *
Semester when the Course Unit is deliveredSummer Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
PAL39RNDr. Radomír Paláček, Ph.D.
SVE0150Ing. Veronika Moškořová, Ph.D.
Summary
Learning Outcomes of the Course Unit
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
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.
Recommended or Required Reading
Required Reading:
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:
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
Individual consultations, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Graded creditGraded credit100 51