The aim of the course is the understanding of basic principles of probability theory and statistical analysis and their application to problems in industrial production and management.  

Probability: concept of probability, conditional probability, multiplicative law, stochastic independence, Bayes rule.

Random variables: discrete and continuous random variables, probability mass function, probability density function, moments (mean, variance), variable transformation, joint probability mass function, covariance, correlation coefficient.

Random variable distributions: uniform, binomial, geometric, Poisson, normal, exponential. Statistical estimates: sampling, point estimates, properties and distributions of estimates, central limit theorem, confidence intervals.

Suggested Literature:

• Μπερτσεκάς Δ.Π., Τσιτσικλής, Γ.Ν., Εισαγωγή στις Πιθανότητες, Εκδόσεις Τζιόλα, 2010.
• Μπερτσεκάς Δ.Π., Τσιτσικλής, Γ.Ν., Εισαγωγή στις Πιθανότητες με Στοιχεία Στατιστικής, Εκδόσεις
  Τζιόλα, 2013.
• Παπούλης Α., Πιθανότητες, Τυχαίες Μεταβλητές και Στοχαστικές Διαδικασίες, Εκδόσεις Τζιόλα, 2002.
• Ross S., Βασικές Αρχές Θεωρίας Πιθανοτήτων, Εκδόσεις Κλειδάριθμος, 2011.
• Montgomery D.C., Runger G.C., Applied Statistics and Probability for Engineers, Wiley, 1994.

Related Scientific Journals:

• Advances in Applied Probability
• Journal of Applied Probability
• Probability in the Engineering and Informational Sciences

Greek

Lectures