The course objective is training students on abstract thinking and acquiring basic knowledge in the field of probability and statistics. The aim is for students to master the basic concepts of probability theory, to enable them to choose the appropriate statistical methods, to perform statistical analysis and to interpret the obtained results.

Student should use the acquired knowledge in further education. The student is qualified to make and solve mathematical models in the further education in professional subjects applying acquired knowledge from probability and statistics.

Basic definitions in probability, conditional probability and Bayes’ formula. Random variable of discrete and continuous type. Two dimensional random variable. Conditional distribution. Numeric characteristics – expectation, dispersion, covariance, correlation. Conditional expectation. Limit theorems. Statistics –general notions. Descriptive statistic analysis (basic notions, data arrangement, table and graphic data presentation, data analysis using descriptive statistic methods, programme support for statistic analysis). Evaluation of unknown parameters (Dot evaluation: momentum method and maximal credibility method).Stochastic processes – general notions.

Lectures, Numerical calculation practice and computer practice . Consultations. Lectures are conducted combining theoretical part of the subject matter with characteristic examples which facilitate understanding. During practice classes, which accompany the lectures, some characteristic problem tasks are done and the presented material is discussed in more detail. In addition to the lecture and practice classes there are regular consultations.