Syllabus
1. From data to model and from model to data.
2. Random variable and its characteristics.
3. Random vectors and their characteristics.
4. Selected discrete and continuous distributions.
5. Normal distribution and distributions derived from it.
6. Limit laws of probability theory and their use in statistics.
7. Principles of estimation theory.
8. Point and interval estimates.
9. Principles of hypothesis testing.
10. U-test, t-test, F-test and their ordinal variants.
11. Goodness-of-fit tests.
12. One, two and multiple choice problem.
13. From data to model revisited.
14. Graphical representation of data, descriptive statistics.
15. Regression analysis.
16. Contingency tables.
17. Selected statistical procedures.
18. Bayesian approach to data analysis.
Annotation
The main aim is to enhance the knowledge from the course Probability and statistics. Attention will be paid especially to the principles of estimation and hypothesis testing, to the theory and applications of the linear model, and to an overview of other useful statistical methods.