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School of Chemical and Environmental Engineering

Now offering two distinct diplomas: Chemical Engineering and Environmental Engineering

Probability and Statistics

1. COURSE INFORMATION:

School Chemical and Environmental Engineering
Course Level Undergraduate
Direction -
Course ID MATH 204 Semester 3rd
Course Category Required
Course Modules Instruction Hours per Week ECTS
Lectures 3
Th=3, E=0, L=0
4
Course Type  Scientific area
Prerequisites  
Instruction/Exam Language Greek
The course is offered to Erasmus students Yes
Course URL https://www.eclass.tuc.gr/courses/MHPER310/  (in Greek)

 

2. LEARNING OUTCOMES

Learning Outcomes

The content / subject of the Probability - Statistics course aims to give the student all those cognitive "tools" to be able to develop skills for: (a) (mathematical) analysis and (b) the modeling of situations / phenomena involving randomness.

Probabilistic thinking (modeling of randomness) is one of the fundamental skills that a modern university education must provide. Its importance has now been recognized in a variety of fields, beyond its applications in science. The use of Probability Theory is fundamental for making decisions in the fields of health sciences, biology, economics, etc. It is also the (prerequisite) basic part of Statistics that is used in data processing and conclusions in social, political, economic sciences, biology, medicine etc.

The Probability - Statistics course introduces students to basic probabilistic models by combining rigorous mathematical approach with intuitive understanding. Its important part is an introduction to Statistics, covering issues of estimation theory, confidence intervals, hypothesis testing, simple and multiple regression, analysis of variance and non-parametric Statistics.

The course is addressed to the sophomore students of the School / Department of Environmental Engineering of the Technical University of Crete.

General Competencies/Skills
  • Work autonomously
  • Review, analyse and synthesise data and information, with the use of necessary technologies)
  • Team work

3. COURSE SYLLABUS

  • Basic topics of Probability Theory
  • Descriptive Statistics
  • Sampling distributions
  • Estimation theory
  • Confidence intervals
  • Hypothesis testing
  • Simple linear regression
  • Multiple linear regression
  • Analysis of variance
  • Non parametric Statistics.

4. INSTRUCTION and LEARNING METHODS - ASSESSMENT

Lecture Method Direct (face to face)
Use of Information and Communication Technology Specialized software, Power point presentations, E-class support
Instruction Organisation Activity Workload per Semester
(hours)
- Lectures 27
- Review exercises 6
-Software applications 6
- Autonomous study 61
Course Total 100

Assessment Method

Ι. Written final examination (100%).
  • Questions of theoretical knowledge.
  • Theoretical problems to be resolved.
II. “Bonus” exercises (10% in addition to the final exam).

OR

I. Midterm and final exam (100%=50%+50%)

II.“Bonus” exercises (10% in addition to the project grade).

5. RECOMMENDED READING

  • T.Daras, P. Sypsas,  “Probability and Statistics: Theory and Applications” ,  2010, Ed. Ziti (In Greek)
  • G.Bamberg, F.Baur, M.Krapp, Statisstics, 2013, Ed. Propobos.
  • Zairis P. “Statistical Methodology, 2010, Ed. Kritiki. 
  • E-class notes

6. INSTRUCTORS

Course Instructor: Associate Professor M. Petrakis (Faculty - ECE)
Lectures: Associate Professor M. Petrakis (Faculty - ECE)
Tutorial exercises:  
Laboratory Exercises: