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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 2016 to 2016
Semester:	winter
E-Credits:	8
Hours per week, examination:	winter s.:4/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time
Additional information:	http://www.karlin.mff.cuni.cz/~kulich/vyuka/ms1

Guarantor:	doc. Mgr. Michal Kulich, Ph.D.
Class:	M Bc. OM M Bc. OM > Povinně volitelné M Bc. OM > Zaměření STOCH
Classification:	Mathematics > Probability and Statistics
Incompatibility :	NSTP201
Pre-requisite :	NMSA202
Interchangeability :	NSTP201
Is co-requisite for:	NMSA332
Is interchangeable with:	NSTP201, NSTP191
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.

Literature - Czech

Last update: prof. RNDr. Jiří Anděl, DrSc. (09.09.2013)

Anděl J.: Matematická statistika, SNTL/ALFA, Praha 1978

Anděl J.: Statistické metody. Matfyzpress, Praha 2007

Anděl, J.: Základy matematické statistiky. Matfyzpress, Praha 2013

Teaching methods -

Last update: T_KPMS (11.05.2012)

Lecture+exercises.

Syllabus -

Last update: doc. Ing. Marek Omelka, Ph.D. (11.04.2018)

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 methods for binary and categorical data.

7. Two-sample methods for categorical data. Contingency tables.

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

9. Correlation analysis. Foundations of linear models.