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Methods of Mathematical Statistics - NMAI061

Title:	Metody matematické statistiky
Guaranteed by:	Department of Software Engineering (32-KSI)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2014 to 2018
Semester:	summer
E-Credits:	5
Hours per week, examination:	summer s.:2/1, 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

Guarantor:	doc. RNDr. Zdeněk Hlávka, Ph.D.
Class:	Informatika Mgr. - Teoretická informatika Informatika Mgr. - Softwarové systémy Informatika Mgr. - Matematická lingvistika
Classification:	Mathematics > Probability and Statistics

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: T_KPMS (20.05.2009)

The main aim is to enhance the knowledge from the course Probability and statistics. Attention will be paid especially to the principles of estimation and hypothesis testing, to the theory and applications of the linear model, and to an overview of other useful statistical methods.

Aim of the course -

Last update: prof. RNDr. Jaromír Antoch, CSc. (06.02.2024)

Basic statistical methods, estimation and testing and their use in practice will be presented.

Literature - Czech

Last update: prof. RNDr. Jaromír Antoch, CSc. (06.02.2024)

Anděl J., Statistické metody, MATFYZPRESS, Praha 1998.

Cipra T., Analýza časových řad s aplikacemi v ekonomii, SNTL/ALFA, Praha 1986.

Zvára K., Regrese, MATFZYPRESS, Praha 2008.

Teaching methods -

Last update: prof. RNDr. Jaromír Antoch, CSc. (06.02.2024)

Lecture+exercises..

Syllabus -

Last update: prof. RNDr. Jaromír Antoch, CSc. (06.02.2024)

Random variable, normal distribution and some related distributions,

central limit theorem.

Random sample, estimator, maximum likelihood method, moment estimators,

confidence intervals, hypothesis testing, t-test, rank tests.

Random vectors, marginal and conditional distributions, graphical

display of multivariate data, multinormal distribution, principal

components.

Linear model, polynomial regression, verification of assumptions,

residuals, leverage, time series analysis, contingency tables,

logistic regression, multivariate statistics.