Learning outcomes
After passing the course a student
· knows how to compute various numerical parameters related to both discrete and continuous random variables;
· knows basic methods of estimation and testing theory;
· is able to characterize the entity of an estimate by its properties (unbiasedness, efficiency, consistency);
· is able to apply the basic methods for deriving estimates (the maximum likelihood, least-squares methods and the method of moments);
· can construct statistical hypothesis in different situations;
· is able to construct interval estimates and handle non-normal data;
· has received training in mathematical statistics that is appropriate for studying field related advanced statistical methods.
Brief description of content
In the part of probability theory, the random events and properties of classical probability are considered. The course covers the theory of random variables and their distributions. In the part of mathematical statistics, the course covers basics of the statistical inference. First, a point estimator, its properties, and methods for finding it are considered. Also, the interval estimation and testing of statistical hypotheses are treated.
