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Analyzing categorical data

Class at Faculty of Mathematics and Physics |
NMST561

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.

Annotation

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