 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 of the course Probability - Statistics aims to give the student all those "tools" to be able to develop skills for: (a) (mathematical) analysis and (b) modeling of phenomena involving randomness (c) processing data and drawing conclusions on problems encountered in the social, political, economic sciences, biology, medicine e.t.c.Upon successful completion of the course, the students will be able to:Use the proper definition/probability theorem or the suitable combinatorics formula in order to compute the probability of a given event.Recognize the basic probability distributions/random variables.Use the proper probability distribution in order to solve problems, characterized by some kind of randomness.Process experimental and numerical data.Use the proper statistical methods for problem solving.Apply statistical methods, with the use of the proper software, to problems of Chemical and Environmental Engineering. General Competencies/Skills Work autonomouslywork in teamsRetrieve, analyze and synthesize data and information, with the use of proper technologies.

3. COURSE SYLLABUS

 Introduction to Probability Theory Ι (probability, combinatorics, independence,  conditional probability).Introduction to Probability Theory ΙΙ (random variables, most commonly used discrete and continuous r.v.s , applications.Central Limiting Theorem (C.L.T.) and somme of its applications.Descriptive Statistics.Sampling distributions.Inductive Statistics .Confidence interval (for mean, proportion, variance, difference of means, difference of proportions, ratio of variances).Hypothesis testing (for mean, proportion, variance, difference of means, difference of proportions, ratio of variances).Simple linear regression.Multiple linear regression.Ανάλυση διασποράς κατά έναν παράγοντα.Non parametric Statistics.Applications using Statistical software  (S.P.S.S  &  Minitab).

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). ORI. Midterm and final exam (100%=50%+50%)II.“Bonus” exercises (10% in addition to the project grade).