Learning Outcomes
The main objective is the understanding of the theory fundamentals and the ability for practical implementation of statistical analysis methods in problems where more than one random variables are involved. In parallel, the course aims at deepening the understanding of basic concepts of probability theory and statistical inference.
Course Content (Syllabus)
Two and higher-dimensional random variables: joint, marginal and conditional distributions. Covariance and correlation. Independent random variables, sums of independent random variables. The bivariate normal distribution.
Analysis of variance: the fixed and random effects models for one factor.
Design of statistical experiments: factorial and fractional factorial experiments, design and statistical analysis. Response surface methodology.
Simple and multiple linear regression, nonlinear regression, correlation.
Description
The final grade M is a combination of the grades in the final written examination (T), the midterm examination (Π) and the project/homework (E) as follows:
• If either Τ < 4,5 or (Τ+Π)/2 < 4, then the final grade is Μ = (0,8)Τ.
• In every other case the final grade is Μ = max {(0,6)Τ + (0,3)Π + (0,2)Ε, (0,8)T}.
Course Bibliography (Eudoxus)
1. Χαλικιάς, Ι.Γ. "Στατιστική, Μέθοδοι ανάλυσης για επιχειρηματικές αποφάσεις", εκδ. Rosili, Αθήνα, 2010.
2. Keller, G. "Στατιστική για οικονομικά και διοίκηση επιχειρήσεων", εκδ. Επίκεντρο, Θεσσαλονίκη, 2010.
3. Μυλωνάς, Ν. και Παπαδόπουλος, Β. "Πιθανότητες & Στατιστική για Μηχανικούς", εκδ. Τζιόλα, Θεσσαλονίκη, 2017.
Additional bibliography for study
- Antony, J., Kaye, M., Experimental Quality, Kluwer, 1999.
- Cobb, G.W., Introduction to Design and Analysis of Experiments, Springer, 2002.
- Montgomery, D.C., Design and Analysis of Experiments, Wiley, 2008.
- Montgomery, D.C., Runger, G.C., Applied Statistics and Probability for Engineers, Wiley, 2006.
- Montgomery, D.C., Runger, G.C., Hubele, N.F., Engineering Statistics, Wiley, 2007.