Data analysis and visualization Uncertainty Modelling Statistical inference I Probability

1. Acquire basic knowledge of Analysis of Variance (ANOVA). 2. Understand non-parametric hypothesis tests and their applications in different contexts. 3. Understand different measures of association and the related hypothesis tests. 4. Gain foundational knowledge of Bayesian statistics. 5. Develop practical skills in the use of statistical software to apply ANOVA, non-parametric tests, measures of association, and Bayesian statistics.

1. Analysis of Variance 1.1. Model assumptions. 1.2. The ANOVA table. 1.3. Inference on pairs of samples. 2. Non-parametric goodness-of-fit tests 2.1. Chi-square test. 2.2. Kolmogorov-Smirnov test. 2.3. Kolmogorov-Smirnov-Lilliefors normality test. 3. Non-parametric location test for a sample 3.1. Test of signs. 3.2. Signed ranges Wilcoxon test. 4. Non-parametric homogeneity tests for several samples 4.1. Chi-square test. 4.2. Kolmogorov-Smirnov test. 4.3. Mann-Whitney-Wilcoxon test. 4.4. Kruskall-Wallis test. 4.5. Homogeneity tests for paired samples. 5. Measures of association between continuous variables 5.1. Pearson linear correlation coefficient. 5.2. Spearman coefficient. 5.3. Kendall coefficient. 6. Introduction to Bayesian inference 6.1. Subjective interpretation of probability. 6.2. Comparison between classical inference and Bayesian inference. 6.3. Bayes Theorem. A priori distribution, likelihood and posterior distribution. 6.4. Beta-binomial model.