1. Types of data; samples and populations; descriptive statistics.
2. Introduction to probability; independence; Bayes theorem.
3. Random variables; probability distributions; quantiles; mean; variance.

4. Discrete distributions: binomial, Poisson; continuous distributions: normal, Student's t, chi-square; central limit theorem.
5. Introduction to estimation and hypothesis testing; confidence intervals.
6. Testing the hypotheses about the mean of one sample of one or two samples: t-tests.

7. Introduction to nonparametric tests.
8. Introduction to analysis of variance.
9. Correlation; simple regression; least squares method; assumptions of regression.

10. Multiple regression models, confounding, choice of the model.
11. Multinomial distribution, goodness-of-fit test, test of independence for discrete variables.

12. Contingency tables, Fisher exact test, McNemar test of symmetry.

13. Designs of epidemiological studies; odds ratio; logistic regression.