APPLIED STATISTICS

- General ideas. From the research problem to probabilistic models. Sampling. Observational and experimental studies. Statistical tests: hypotheses, p-values and their interpretation, types of error, power. The problem of multiple comparisons/tests. Confidence intervals.
- Elementary methods. Inference on a proportion and comparison of two proportions. Student's t: one sample, two samples, paired data. Large sample inference. Nonparametric methods: Wilcoxon (one and two samples) and Kruskal-Wallis tests. Correlation coefficient.
- Advanced methods. One-way and two-way analysis of variance. Regression analysis: linear and logistic model. Exploring multivariate data: principal components and cluster analysis.