Probability and Statistics

1. COURSE INFORMATION:

School	Chemical and Environmental Engineering
Course Level	Undergraduate
Direction	-
Course ID	MATH 204	Semester	3^rd
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:

3rd Semester

Technical University of Crete

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