SCHOOL

ENGINEERING

ACADEMIC UNIT

ELECTRICAL & COMPUTER ENGINEERING

LEVEL OF STUDIES

Undergraduate

COURSE CODE

ECE_Υ322

SEMESTER

3^rd

COURSE TITLE

PROBABILITY & STATISTICS

INDEPENDENT TEACHING ACTIVITIES
if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

WEEKLY TEACHING HOURS

CREDITS

Theory Lectures and Recitations

4hrs/wk

4

Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

COURSE TYPE

general background,
special background, specialised general knowledge, skills development

Skills development course 

PREREQUISITE COURSES:

Single-variate calculus (ECE_Y101)

Multi-variate Calculus & Vector Analysis (ECE_Y212)

LANGUAGE OF INSTRUCTION and EXAMINATIONS:

Greek

IS THE COURSE OFFERED TO ERASMUS STUDENTS

Yes

COURSE WEBSITE (URL)

https://eclass.upatras.gr/courses/EE764/

Learning outcomes

The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

Consult Appendix A

·    Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area

·    Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B

·    Guidelines for writing Learning Outcomes

After a successful completion of the course the student will be expected to know the basic laws of stochasticity and probability theory, to solve combinatorial problems, to understand the notions of single- or bi-variate random variables and their distributions as well as being able to calculate probabilities of events or conditions for a given random variable. To model stochastic phenomena with the help of known distributions and be able to describe their behaviour with the help of the distribution parameters. From the Statistics point of view, the students should be able to effectively apply data analysis techniques in order to describe the basic characteristics of a dataset, and also statistical inference and linear regression analysis to suitably selected samples in order to infer statistically correct conclusions about different characteristics of the populations where the samples come from.

 General Competences

Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Adapting to new situations

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

Project planning and management

Respect for difference and multiculturalism

Respect for the natural environment

Showing social, professional and ethical responsibility and sensitivity to gender issues

Criticism and self-criticism

Production of free, creative and inductive thinking

……

Others…

…….

The course aims at developing skills mainly for independent work and occasionally for team and collaborative work, for effective decision making, as well as for promoting free, creative and inductive thinking.

Introduction to probability. Counting techniques and applications. Conditional probability. Univariate and multivariate random variables. Cumulative distribution functions, probability functions and probability density functions. Functions of random variables. Independence οf random variables. Conditional distributions. Moments, moment generating functions and characteristic functions. Covariance and correlation. Conditional expectation and variance. Applications of useful distributions: Bernoulli, binomial, multinomial, hypergeometric, geometric, negative binomial, Poisson, uniform, exponential, Gamma, Beta, Weibull, normal, lognormal, χ2, t, F and the multivariate normal. The Poisson stochastic process. Inequalities and limit theorems. Reliability and hazard rate. The exponential and Weibull distributions in reliability.
 Random sampling. Descriptive statistics. Sampling distributions and normal distribution. Basic principles of point estimation. Interval estimation. Statistical Intervals on the mean, proportion and variance of one population. Statistical Intervals on a difference in means, on a difference in proportions and on the ratio of two variables. Simple linear regression.

DELIVERY
Face-to-face, Distance learning, etc.

Face to Face in classroom

USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
Use of ICT in teaching, laboratory education, communication with students

Lectures are performed with the help of viewgraphs projected in class simultaneously with the use of whiteboard for better understanding of the analytical techniques needed in theory and exercises. In addition, the electronic platform “eclass” of the University is used for better support of students’ educational needs and also for communicating with them. The   

TEACHING METHODS

The manner and methods of teaching are described in detail.

Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

STUDENT PERFORMANCE EVALUATION

Description of the evaluation procedure

Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

Written final exam in Greek Language which consists of:

¬  4 exercises (over the full length of the material) that require full development of a solution and justification

¬  4-5 multiple choice or true/false questions which require critical thinking and in-depth understanding of the material

¬  «Applied Probability and Statistics», by I.A. Koutrouvelis, Editor: Gotsis  (in greek)

¬  «Probability and Statistics for Engineers», G. Zioutas, Editor: “Sofia” (in greek)

¬  "Applied Statistics and Probability for Engineers", D.C. Montgomery, G.C. Runger, John Wiley & Sons.