Theory teaching
Experiment and random event. Space of elementary events. Relationships and operations with events. An axiomatic definition of probability. Classical definition of probability. Conditional probability, independence of events. The complete probability formula and the Bayes formula. Random variables, density, and distribution function. Discrete and continuous random variables. Some more important probability distribution functions of discrete random variables: Binomial (Bernoulli), Poisson, Uniform, Geometric, Exponential. Some important probability distribution functions of continuous random variables: Normal, gamma, exponential, beta. Mathematical expectation, variance, moments and correlation coefficient. Limit theorems: Poisson’s and normal approximation of binomial distribution, law of large numbers, Central Limit theorem. Basic concepts of statistics: log-normal, student, chi squared, Phisher’s distributions. Interval estimation of distribution parameters. Hypothesis testing, variance analysis. Linear, nonlinear and n-dimensional regression, Gauss’s least squares method, Spearman’s test for range correlation

Practical teaching
Creating tasks that correspond to the material in the lectures.

1. Milan Merkle, Verovatnoća i statistika za inženjere i studente tehnike, Akademska misao, Beograd, 2006.
2. Biljana Popović i Miroslav Ristić, Statistika za geografe, Niš, 2006.
3. Vesna Jevremović, Verovatnoća i statistika (Matematika III), Građevinski fakultet i KRUG, Beograd, 1999.
4. Slobodan Vujić i Aleksandar Ivić, Matematičke metode u rudarstvu i geologije, RGF, Univerzitet u Beogradu, 1991.
5. Sheldon M. Ross, Introduction to Probability and Statistics for Engineers and Scientists, 3rd edition, Elsevier Academic Press, 2004.