Syllabus of lectures and tutorials
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1. Combinatorics. Random events, operations with them, sample space.
2. Definitions of probability events - classical, geometrical, statistics. Conditional probability, Bayes' theorem. Bernoulli independent repeated trials.
3. Discrete random variable and continuous random variable. Functions of random variables. Moment-generating function, quantiles.
4. Discrete probability distribution.
5. Continuous probability distribution.
6. Random vector. The probability distribution. Expected value, covariance, coefficient of correlation.
7. Statistics. Statistical methods, descriptive statistics
8. Observed data. Point estimators, interval estimators.
9. Statistical hypothesis testing – the testing process, interpretation, importance. Parametric and non-parametric tests.
10. Correlation and regression analysis.
11. Time series.
12. Forecasting. Time series methods.
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1. Combinatorics. Random events, operations with them, sample space.
2. Definitions of probability events - classical, geometrical, statistics. Conditional probability, Bayes' theorem. Bernoulli independent repeated trials.
3. Discrete random variable and continuous random variable. Functions of random variables. Moment-generating function, quantiles.
4. Discrete probability distribution.
5. Continuous probability distribution.
6. Random vector. The probability distribution. Expected value, covariance, coefficient of correlation.
7. Statistics. Statistical methods, descriptive statistics
8. Observed data. Point estimators, interval estimators.
9. Statistical hypothesis testing – the testing process, interpretation, importance. Parametric and non-parametric tests.
10. Correlation and regression analysis.
11. Time series.
12. Forecasting. Time series methods.