Probability Theory and Statistics

Descriptive statistics: data summary and presentation, frequency distribution, histogram, characteristic values (mean, median, mode, range, variance, standard deviation). Probability theory: basic concepts, events, conditional probability, addition and multiplication law of probabilities, Bayes theorem. Probability distributions, discrete and continuous random variables, expected value, variance and standard deviation. Important distributions: Bernoulli, binomial, geometric, Poisson, uniform, exponential, gamma, normal distribution and the central limit theorem, Student, X2 and F distributions. Statistical estimation: sampling distributions, point estimation, properties of estimators, confidence intervals. Statistical hypotheses: hypothesis testing, type I and type II errors, required sample size, goodness of fit tests.

After the completion of the course the students should be able to
apply the basic concepts and techniques of probability theory and
statistical inference.

Mathematic Analysis

Final written exam (compulsory), Intermediate written exam and/
or assignments (optional)

1. Statistics, D. P. Psoinos. Zitis Publ., 1999.
2. Probability and Statistics for Engineers, G. Ch. Zioutas, Zitis
Publ., 2013.