Course Information
SchoolMechanical Engineering
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorSofia Panagiotidou
Course ID20000305

Programme of Study: UPS of School of Mechanical Engineering

Registered students: 392
OrientationAttendance TypeSemesterYearECTS
CoreCompulsory Course326

Class Information
Academic Year2016 – 2017
Class PeriodWinter
Faculty Instructors
Weekly Hours5
Class ID
Course Type 2016-2020
  • Background
Course Type 2011-2015
General Foundation
Mode of Delivery
  • Face to face
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Learning Outcomes
Understanding and ability to apply the basic concepts and techniques of probability theory and 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)
Descriptive statistics: data summary and presentation, frequency distribution, histogram, characteristic values. Probability and probability distributions: basic concepts, events, conditional probability and Bayes theorem. Probability distributions, discrete and continuous random variables, expected value, variance and standard deviation, moment generating function. Important distributions: 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, required sample size. Statistical hypotheses: type I and type II errors, hypotheses on parameters, goodness of fit tests. Simple linear regression.
Statistics, Probability
Educational Material Types
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Problem solving on the computer using spreadsheets and specialized software. Educational material and information about the course are available at eLearning (
Course Organization
Reading Assigment1003.3
Written assigments110.4
Student Assessment
The final grade (M) is a combination of the grade in the final exam (B) and that of the homework project (E) as follows: - If B < 4,3 then M = B. - If Β ≥ 4,3 then Μ = Β + (0,1)Ε.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Exam with Short Answer Questions (Summative)
  • Written Exam with Extended Answer Questions (Summative)
  • Written Assignment (Summative)
  • Homework (Summative)
Course Bibliography (Eudoxus)
1. Ζιούτας, Γ.Χ. "Πιθανότητες και στατιστική για μηχανικούς", εκδ. Σοφία, Θεσσαλονίκη, 2012. 2. Ζαχαροπούλου, Χ. "Στατιστική, μέθοδοι – εφαρμογές, τόμος Α’", εκδ. Σοφία, Θεσσαλονίκη, 2012. 3. Αγγελής, Β. και Δημάκη, Κ. "Στατιστική,τόμος Α'", εκδ. Σοφία, Θεσσαλονίκη, 2011. 4. Ψωινός, Δ.Π. "Στατιστική", εκδ. Ζήτη, Θεσσαλονίκη, 1999. 5. Keller, G. "Στατιστική για οικονομικά και διοίκηση επιχειρήσεων", εκδ. Επίκεντρο, Θεσσαλονίκη, 2010.
Additional bibliography for study
- 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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