Subjects(version: 867)

Analyzing categorical data - NMST561
Title: Analýza kategoriálních dat Department of Probability and Mathematical Statistics (32-KPMS) Faculty of Mathematics and Physics from 2018 winter 3 winter s.:2/0 Ex [hours/week] unlimited unlimited not taught Czech full-time
Guarantor: prof. RNDr. Jiří Anděl, DrSc. M Mgr. PMSEM Mgr. PMSE > Volitelné Mathematics > Probability and Statistics
 Annotation - ---CzechEnglish
Last update: T_KPMS (27.04.2015)
Modern statistical methods for analysis of categorical data. Theoretical principles are demonstrated on numerical data using program R.
 Aim of the course - ---CzechEnglish
Last update: T_KPMS (27.04.2015)

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

 Course completion requirements - Czech
Last update: RNDr. Jitka Zichová, Dr. (29.10.2019)

Ústní zkouška.

 Literature - Czech
Last update: T_KPMS (27.04.2015)

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

 Teaching methods - ---CzechEnglish
Last update: T_KPMS (27.04.2015)

Lecture.

 Requirements to the exam - Czech
Last update: RNDr. Jitka Zichová, Dr. (13.10.2017)

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.

 Syllabus - ---CzechEnglish
Last update: T_KPMS (27.04.2015)

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.

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