Ability of abstract thinking and acquiring basic knowledge in the area of probability and statistics and stochastic processes

Student is competent to design and solve mathematical models in the field of probability and statistics in further education and professional courses.

Basic definitions in probability, conditional probability and Bayes’ formula. Random variable of discrete and continuous type, distribution functions. Two dimensional random variable. Numeric characteristics – expectation, dispersion, covariance, correlation. Limit theorems. Population, sample. Sampling techniques. Descriptive statistics, point estimate and interval estimate. Parametric and nonparametric hypotheses and significance testing. Statistical inference. Regression analysis, linear, nonlinear and logistic regression. Statistical data visualization, graphing data. Statistical models (queuing theory, Monte Carlo modeling). Statistical package “R”.

Lectures; Numerical computing practice. Consultations. Lectures are combined. In lectures, theoretical part of the course is followed by typical examples for better understanding. In practice, which accompanies lectures, typical problems are solved and knowledge from the lectures is deepened. Besides lectures and practice, consultations are held on a regular basis. Part of the course, presenting a logical whole, can be passed during the teaching process in the form of the following 2 modules (the first module: probability, the second module: statistics. The oral part of the examination is not obligatory.