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Course, academic year 2022/2023
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Mathematical Statistics 1 - NMSA331
Title: Matematická statistika 1
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2022
Semester: winter
E-Credits: 8
Hours per week, examination: winter s.:4/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Additional information:
Guarantor: doc. RNDr. Arnošt Komárek, Ph.D.
doc. Ing. Marek Omelka, Ph.D.
Class: M Bc. OM
M Bc. OM > Povinně volitelné
M Bc. OM > Zaměření STOCH
Classification: Mathematics > Probability and Statistics
Pre-requisite : NMSA202
Is co-requisite for: NMSA332
Is pre-requisite for: NMSA351
Is interchangeable with: NSTP191, NSTP201
In complex pre-requisite: NMSA349
Annotation -
Last update: G_M (16.05.2012)
Foundations of statistical methods. Recommended for bachelor's program in General Mathematics, specialization Stochastics.
Aim of the course -
Last update: G_M (16.05.2012)

The students will become familiar with basic methods for statistical data analysis.

Course completion requirements -
Last update: doc. Ing. Marek Omelka, Ph.D. (17.09.2020)

Requirements to get the exercise class course credit.

1. A successfully solved midterm test (at least 60 points from 100).

2. Well solved home tasks.

The nature of these requirements precludes any possibility of additional attempts to obtain the exercise class credit.

Literature -
Last update: doc. Ing. Marek Omelka, Ph.D. (28.10.2019)

Mukhopadhyay, N. (2000). Probability and statistical inference. CRC Press

Teaching methods -
Last update: T_KPMS (11.05.2012)


Requirements to the exam -
Last update: doc. Ing. Marek Omelka, Ph.D. (28.10.2019)

The requirements for the oral exam are in agreement with the syllabus of the course as presented during lectures.

Syllabus -
Last update: doc. Ing. Marek Omelka, Ph.D. (22.09.2019)

1. Random sample. Distribution of sample mean and variance. Order statistics.

2. Point and interval estimates - basic principles. Empirical estimates, sample moments and quantiles.

3. Hypothesis testing principles.

4. One-sample and paired methods for quantitative data.

5. Two-sample methods for quantitative data.

6. One-sample and two-sample methods for binary adata.

7. Multinomial distributions and contingency tables.

8. Multi-sample methods for quantitative data. Analysis of variance. Multiple comparison principles.

9. Correlation analysis.

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