Program overview Part I Axioms, conditional probability, Bayes rule, combinatorial analysis. Random variables: functions of distribution, mass and density, expectation and variance. Discrete and continuous probability laws. Reliability. Random vectors: correlation, multinormal distribution, central limit theorem Stochastic processes: Markov chains, Poisson process, Brownian motion. Descriptive statistics: diagrams, calculation of characteristics. Sampling distributions: estimate, mean squared error, confidence intervals. - Hypothesis tests: parametric tests and fit test.