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Multivariate Analysis - NMST539

Title:	Mnohorozměrná analýza
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2014 to 2014
Semester:	summer
E-Credits:	5
Hours per week, examination:	summer s.:2/2, 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. Jan Hurt, CSc.
Class:	M Mgr. PMSE M Mgr. PMSE > Povinně volitelné
Classification:	Mathematics > Probability and Statistics
Incompatibility :	NSTP018
Interchangeability :	NSTP018
Is interchangeable with:	NSTP018

Opinion survey results Examination dates Schedule Noticeboard

Files		Comments	Added by
	notes0.pdf	Lecture Notes part O	doc. RNDr. Ivan Mizera, CSc.
	notes1.pdf	Lecture Notes part I	doc. RNDr. Ivan Mizera, CSc.
	notes2.pdf	Lecture Notes part II	doc. RNDr. Ivan Mizera, CSc.
	problems.pdf	Homework problems	doc. RNDr. Ivan Mizera, CSc.

Annotation -

Last update: doc. Ing. Marek Omelka, Ph.D. (08.12.2020)

Normal, Wishart and Hotelling distributions. Canonical correlations. Principal components. Factor, discriminant and cluster analyses. Use of libraries of statistical programs.

Aim of the course -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (08.12.2020)

To explain selected methods of multivariate statistics.

Literature - Czech

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (08.12.2020)

Hebák P., Hustopecký J.: Vícerozměrné statistické metody s aplikacemi. SNTL-Alfa. Praha, 1987

Mardia K.V., Kent J.T., Bobby J.M.: Multivariate Analysis. Academia Press. London, 1979

Rao C.R.: Linear Statistical Inference and Its Applications. 2nd edition. Wile. New York, 1973. (existuje český překlad)

Teaching methods -

Last update: T_KPMS (16.05.2013)

Lecture+exercises.

Syllabus -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (08.12.2020)

Normal, Wishart and Hotelling distributions. Canonical correlations. Principal components. Factor, discriminant and cluster analyses. Use of libraries of statistical programs.