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Probability and Statistics - NSTP129

Title:	Pravděpodobnost a statistika
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2018
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:	cancelled
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	RNDr. Jitka Zichová, Dr.
Classification:	Mathematics > Probability and Statistics
Incompatibility :	{NUMP013 a NUMP023}, NMAI059, NSTP014, NSTP022, NSTP070, NSTP177
Interchangeability :	NMFM202, NSTP022
Is incompatible with:	NMUE012, NMUE032, NSTP177, NSTP014, NSTP070, NSTP017

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: G_M (20.05.2011)

Introductory course. Foundations of probability (probability, random variables and their characteristics, law of large numbers, central limit theorems), elements of mathematical statistics( point and interval estimations, hypothesis testing for simple models, linear regression, contingency table).

Aim of the course -

Last update: T_KPMS (22.05.2008)

To explain foundations of probability theory and mathematical statistics.

Literature - Czech

Last update: T_KPMS (22.05.2008)

Karel Zvára, Josef Štěpán: Pravděpodobnost a matematická statistika. Matfyzpress, Praha 2002.

Václav Dupač, Marie Hušková: Pravděpodobnost a matematická statistika. Karolinum, Praha, 1999.

Jiří Anděl: Základy matematické statistiky. Matfyzpress, Praha 2005.

Alfred Rényi: Teorie pravděpodobnosti. Academia, Praha, 1972.

Teaching methods -

Last update: G_M (28.05.2008)

Lecture+exercises.

Syllabus -

Last update: T_KPMS (18.05.2010)

Foundations of probability :probability, random variablesm and their characteristics, law of large numbers, central limit theorems.

Elements of mathematical statistics: point and interval estimations, hypothesis testing for simple models, linear regression,

contingency table.