1. solve problems of evaluating probability of a given event
2. recognize specific discrete or continuous distribution
3. solve problems of evaluating expectation and variance of some distribution
4. analyze given data
5. solve problems of point and interval estimation
6. use statistical tests
7. demonstrate ability for mathematical modelling
8. use critical thinking

4 hours per week

1 hour per week

each student must solve some problems on their own

6 hours per semester, 4 out of those 6 hours students work on their own

1. Probability; equally likely outcomes; geometric probability.
2. Conditional probability; independence; law of yotal probability; Bayes' rule.
3. Discrete random variables and random vectors; marginal distribution; conditional distribution.
4. Moments; characteristic function; generating functions.
5. Geometric Distribution; Binomial Distribution; Poisson Distribution.
6. Random variables; probability distributions; probablitiy densities; Functions of random variables.
7. Exponential distribution; normal distribution.
8. Midterm exam.
9. Random vectors; conditional probability distributions.
10. Functions of random vectors; Law of large numbers and central limit theorem.
11. Measures of central tendency (mean, median, mode); measures of dispersion (standard deviation, variance, quantile, and IQR); Unbiased point estimations; Maximal-likelihood estimation.
12. Interval estimations; confidence intervals; Confidence Intervals for parameters of normal distribution.
13. Hypothesis testing; type of errors; parametric hypothesis testing; sampling distributions.
14. Hypothesis testing; type of errors; parametric hypothesis testing; sampling distributions; Pearson's Chi-squared Test (Goodness-of-fit tests, tests of independence and homogeneity).
15. Final exam.