The course will cover following topics:
• Statistical decision making: Formulation of null and alternative hypotheses.

1st /2nd -type errors, the significance level, p-level of the test.
• One-sample tests: Parametric tests of mean value, esp. in normal and binomial distribution. Nonparametric tests.
• Two-sample tests: Two-sample t-test, two-sample nonparametric
tests. Prerequisites for using parametric tests and verification of the assumptions.
• Paired tests: Paired t-test, nonparametric paired tests.
• Multiple samples: Analysis of variance (on-way, multiple-way). Inter-
actions. Kruskal-Wallis test.
• Distribution difference testing: Kolmogorov-Smirnov test. Testing
normality - Shapiro-Wilk test, d’Agostino and Anscombe test.
• Testing of homogeneity of variances : Fisher's and Levene's test.
• Continuous variables dependency testing:
- Correlation and its testing. Coherence of several characters - partial coefficient
correlation.
- regression models: Regression linear model. Parameter estimates. Coefficient
of determination. Connection with ANOVA. Generalization of the linear model:
quadratic and multidimensional regression. analysis of residuals.
• Testing the dependence of nominal variables: - contingency tables
(independence, homogeneity of samples, symmetry, residues, graphical representation).
Measures of association. Use of χ 2 statistics to verify the fit of the distribution.
• Testing the dependence of ordinal quantities: Spearman's test, Kendall's
τ and derived measures.