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Analyzing categorical data - NMST561

Title:	Analýza kategoriálních dat
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2018
Semester:	winter
E-Credits:	3
Hours per week, examination:	winter s.:2/0, 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

Guarantor:	prof. RNDr. Jiří Anděl, DrSc.
Class:	M Mgr. PMSE M Mgr. PMSE > Volitelné
Classification:	Mathematics > Probability and Statistics

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Annotation -

Modern statistical methods for analysis of categorical data. Theoretical principles are demonstrated on numerical data using program R.

Last update: T_KPMS (27.04.2015)

Aim of the course -

Statistical methods for analyzing categorical data are presented. The identification of model is shown for one-dimensional and multidimensional data.

Last update: T_KPMS (27.04.2015)

Course completion requirements - Czech

Ústní zkouška.

Last update: Zichová Jitka, RNDr., Dr. (29.10.2019)

Literature - Czech

Simonoff J. S. (2003): Analyzing Categorical Data. Springer, New York.

Last update: T_KPMS (27.04.2015)

Teaching methods -

Lecture.

Last update: T_KPMS (27.04.2015)

Requirements to the exam - Czech

Zkouška je ústní a zahrnuje následující tematické okruhy:

Binomické, Poissonovo a multinomické rozdělení. Rozklad Pearsonovy statistiky. Mocninné divergence, disparita a index nepodobnosti.

Výpočet rozsahu výběru. Modely underdisperzních a overdisperzních rozdělení. Kontingenční tabulky.

Last update: Zichová Jitka, RNDr., Dr. (13.10.2017)

Syllabus -

Binomial distribution: confidence intervals, testing hypotheses, calculating sample size, exact inference, testing homogeneity, rule of three.

Poisson distribution: asymptotic inference, exact inference.

Multinomial distribution: power divergencies, disparity, Benford’s law, decomposition of Pearson statistic.

Over-dispersed and under-dispersed distributions.

Contingency tables: tests of independence, measures of dependence, iterative proportional fitting procedure, median polish procedure, correspondence analysis, tables with ordered categories, paired data, identification of model.

Last update: T_KPMS (27.04.2015)