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Mathematical Statistics A - NMST711

Title:	Matematická statistika A
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2023
Semester:	summer
E-Credits:	6
Hours per week, examination:	summer s.:2/2, C+Ex []
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:	https://dl1.cuni.cz/course/view.php?id=11676

Guarantor:	doc. RNDr. Zdeněk Hlávka, Ph.D. doc. Ing. Marek Omelka, Ph.D.
Classification:	Mathematics > External Subjects, Probability and Statistics

Opinion survey results Examination dates SS schedule Noticeboard

Annotation -

Last update: G_M (07.05.2014)

Multivariate statistical analysis, nonparametric statistical procedures (rank tests, nonparametric regression), bootstrap method. A course for the students of the Faculty of Social Sciences, Charles University.

Aim of the course -

Last update: G_M (07.05.2014)

Selected chapter from mathematical statistics useful in data analysis

Course completion requirements -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (06.10.2017)

Conditions for obtaining the credit (zápočet): active participation in the exercises and successful solving of a written assignment. The nature of the credit does not allow it to be repeated. Acquiring credit is a prerequisite for attending the exam.

Teaching methods -

Last update: G_M (07.05.2014)

Lecture+exercises.

Requirements to the exam -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (06.10.2017)

Requirements for the oral exam correspond to the syllabus of the subject in the scope that was presented at the lecture.

Syllabus -

Last update: G_M (07.05.2014)

Multivariate statistical analysis. Nonparametric statistical procedures (rank tests, nonparametric regression). Bootstrap.

Entry requirements -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (25.05.2018)

basic knowledge of probability and statistics, matrix algebra