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Last update: T_KPMS (13.05.2014)
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Last update: doc. Mgr. Michal Kulich, Ph.D. (05.09.2013)
Students will understand the foundations of mathematical statistics and important principles of parameter estimation and hypotheses testing. They will become familiar with most common statistical procedures and their application to real data.
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Last update: doc. RNDr. Matúš Maciak, Ph.D. (16.10.2023)
Obtaining 'zápočet' (i.e. passing the tutorial) is a necessary condition for taking the examination.
It is granted by the teacher for (i) presence during the exercises (maximum of 2 unattended exercises allowed) and (ii) passing 2 written exams during the semester (at least 50% of points in each written pass exam).
There are no additional terms or possibilities for obtaining 'zápočet'.
The exam has a written and an oral part covering everything that is presented during the lectures. For more information see 'Requirements to the exam'. |
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Last update: doc. RNDr. Michal Pešta, Ph.D. (28.10.2019)
Anděl J.: Statistické metody. MATFYZPRES, Praha 1998
Anděl J.: Základy matematické statistiky. MATFYZPRES, Praha 2002
Casella G, Berger R.L.: Statistical Inference, 2nd Edition. Duxbury Thomson Learning, Pacific Grove, CA, 2002 |
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Last update: G_M (24.04.2012)
Lecture+exercises. |
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Last update: doc. RNDr. Michal Pešta, Ph.D. (28.10.2019)
Obtaining "zápočet" (i.e. passing the tutorial) is a necessary condition for taking the examination.
We require knowledge of the concepts introduced in all the discussed fields, of their relations, and of all the performed proofs. |
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Last update: doc. Mgr. Michal Kulich, Ph.D. (05.09.2013)
1. Random sample and its properties. 2. Point and interval estimators and their properties. 3. Parameter estimation methods. Empirical, moment estimators. Maximum likelihood. 4. Theory of hypotheses testing. 5. One-sample and paired methods for continuous data. 6. One-sample methods for discrete data. 7. Two-sample methods for continuous data. 8. Contingency tables. 9. Analysis of variance. 10. Linear regression. |