Subjects

Your browser does not support JavaScript, or its support is disabled. Some features may not be available.

Mathematical Statistics 2 - NSTP002

Title:	Matematická statistika 2
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2010
Semester:	summer
E-Credits:	9
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:	cancelled
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	prof. RNDr. Jiří Anděl, DrSc.
Class:	Ekonometrie Mat. statistika Teorie pravděpodobnosti
Classification:	Mathematics > Probability and Statistics
Interchangeability :	{NSTP202 a NSTP192}
Co-requisite :	NSTP001
Incompatibility :	NSTP202
Is incompatible with:	NSTP124

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: T_KPMS (25.04.2008)

The lecture is devoted to fundamental methods of estimation theory and hypotheses testing. Further, some practical applications (ANOVA, nonparametrics, contingency tables) are introduced. The course provides a basis for studying advanced statistics.

Aim of the course -

Last update: T_KPMS (15.05.2008)

The students learn fundamental methods used in theory of estimation and testing hypotheses. The methods are applied to constructing practical procedures for statistical analysis of real data, e.g. analysis of variance, nonparametrics, contingency tables and so on.

Literature - Czech

Last update: T_KPMS (18.04.2008)

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

Anděl J.: Statistické metody. Matfyzpress, Praha 1998

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

Teaching methods -

Last update: G_M (27.05.2008)

Lecture+exercises.

Syllabus -

Last update: T_KPMS (25.04.2008)

Theory of estimation (unbiasedness, consistency and efficiency of estimators, Rao-Cramér inequality, Fisher information, Bhattacharya theorem, sufficient statistics, Lehmann-Scheffé theorem, ancillary statistics, Basu theorem, Rao-Blackwell theorem, maximum likelihood method). Tests of statistical hypotheses. Linear models

and their applications. Scheffé and Tukey methods for multiple comparisons. One-way and two-way analysis of variance. Nonparametric methods (sign test, one-sample and two-sample Wilcoxon test, Kruskal-Wallis test, Friedman test, Spearman correlation coefficient). Tests of fit. Tests for contingency tables.