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Probability and Mathematical Statistics - NSTP022

Title:	Pravděpodobnost a matematická statistika
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2013 to 2017
Semester:	summer
E-Credits:	8
Hours per week, examination:	summer s.:4/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	doc. RNDr. Daniel Hlubinka, Ph.D. prof. RNDr. Marie Hušková, DrSc.
Classification:	Mathematics > Probability and Statistics
Incompatibility :	{NUMP013 a NUMP023}, NMAI059, NSTP014, NSTP070, NSTP177
Pre-requisite :	{Math. Analysis 1a, 1b}
Co-requisite :	NMAA069
Interchangeability :	NMSA202
Is incompatible with:	NMUE012, NMUE032, NMAI016, NMSA202, NSTP129, NSTP177, NSTP017, NHII031, NSTP070, NSTP014
Is interchangeable with:	NMSA202, NSTP129, NMAI016
Is complex co-requisite for:	NMOD009

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: G_M (10.10.2001)

Foundations of probability theory (axiomatic definition of probability, conditional probability, random vectors and their characteristics, limit theorems). Basic statistical tasks (point and interval estimations, hypothesis testing for simple models).

Aim of the course -

Last update: T_KPMS (22.05.2008)

Foundations of probability theory and mathematical statistics

Literature - Czech

Last update: T_KPMS (14.05.2003)

Dupač V., Hušková, M.: Pravděpodobnost a matematická statistika, Karolinum, 1999, 2001.

Likeš J., Machek J.: Matematická statistika, SNTL, 1983

Anděl J.: Matematická statistika, SNTL, 1978 (některé paragrafy)

Anděl J.: Statistické metody, Matfyzpress, 1993 (některé paragrafy)

Teaching methods -

Last update: G_M (27.05.2008)

Lecture+exercises.

Syllabus -

Last update: T_KPMS (14.05.2003)

Foundations of probability theory (axiomatic definition of probability, conditional probability, random vectors and their characteristics, limit theorems).

Basic statistical tasks (point and interval estimations, hypothesis testing for simple models).