Course info

The course is designed to provide students with theoretical background of and professioan skills in statistical data analysis. The educational objectives are: 

1. to introduce the students to the main concepts of probability and statistics; 
2. to provide the students with the methodologies, the practical techniques, and the software tools related to probabilistic reasoning, regression, descriptive and inferential statistics.

The course consists of theoretical lessons interspersed with laboratory sessions with the R software (https://www.r-project.org/). 

1. Introduction to probability. Probability diagrams, conditional probability.
2. Bayes’ theorem
3. Random variables, discrete and continues distributions.
4. Position indices and variability indices of random variables distribution.
5. First R session: probabilities with R.
6. Properties of mean and variance.
7. Central limit theorem
8. Confidence intervals on mean and variance.
9. Second R session: confidence intervals with R.
10. Hypothesis testing and ANOVA
11. Test of independence
12. Third R session: hypothesis tests with R.
13. Relations between confidence intervals and hypothesis test
14. Good of fitness tests.
15. Fourth R session: Good of fitness tests with R.
16. Correlation
17. Linear and Logistic regression with one and multiple variables
18. Fifth R session: regression in R.
19. Time series
20. Fifth R session: time series in R
21. Probabilistic models (EM)
22. Sixth R session: probabilistic models in R.