Course Information
SchoolMechanical Engineering
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorSofia Panagiotidou
Course ID20000307

Programme of Study: UPS of School of Mechanical Engineering

Registered students: 87
OrientationAttendance TypeSemesterYearECTS
EnergyElective Courses belonging to the other745
Design and StructuresElective Courses belonging to the other745
Industrial ManagementCompulsory Course belonging to the selected specialization (Compulsory Specialization Course)745

Class Information
Academic Year2021 – 2022
Class PeriodWinter
Instructors from Other Categories
Weekly Hours4
Class ID
Course Type 2021
Specialization / Direction
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Required Courses
General Prerequisites
The course "Statistics" is not formally a prerequisite but sufficient knowledge of its content is essential for the effective understanding of the concepts in "Multivariate Statistics".
Learning Outcomes
1.Understand basic concepts of probability theory 2.Understand the statistical analysis methods in problems where more than one random variables are involved 3.Recognize the importance of statistical experiments 4.Apply computer software (SPSS, Data analysis, Minitab) for statistical data analysis 5.Interpret the results of statistical inference
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Advance free, creative and causative thinking
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.
Statistics, Probability
Educational Material Types
  • Notes
  • Slide presentations
  • Book
  • Computer software
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Utilization of specialized computer software for problem solving. Posting of announcements, notes and information regarding the course at eLearning (
Course Organization
Reading Assigment742.5
Interactive Teaching in Information Center60.2
Written assigments130.4
Student Assessment
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}.
Student Assessment methods
  • Written Exam with Short Answer Questions (Summative)
  • Written Assignment (Summative)
  • Written Exam with Problem Solving (Summative)
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.
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