SubjectsSubjects(version: 867)
Course, academic year 2019/2020
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 [hours/week]
Capacity: unlimited
Min. number of students: unlimited
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
Annotation -
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 -
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 -
Last update: T_KPMS (27.04.2015)


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 -
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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