COURSE INFORMATION Course Title: PROBABILITY AND STATISTICS FOR ENGINEERS

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
MTH 205	A	3	2	2	0	3	5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Erind Bedalli
Second Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Compulsory
Classroom and Meeting Time:	A127 & A005
Course Description:	This course provides an elementary introduction to probability and statistics with applications. Descriptive statistics. Sets, events, and probability. Concept and definition of random variables and different functions of random variables. Both univariate and multivariate functions will be discussed. Discrete (binomial distribution, Poisson’s distribution) and continuous distribution functions (normal, lognormal, exponential distribution, gamma distribution), with the focus to commonly used probability distribution functions in civil engineering. Statistical estimation and testing; confidence intervals; and an introduction to linear regression. Statistics of extreme events. Testing of hypothesis. Engineering application.
Course Objectives:	The course will firstly introduce the concepts of finite probability theory and continuous probability theory. In the second part, the course addresses the statistical processes of formulating questions, collecting and analyzing data, and interpreting results. Methods related to descriptive and inferential statistics and the concept of probability are studied.

COURSE OUTLINE

Week	Topics
1	Introduction to probability.Random experiments, sample spaces and events. Axioms of probability.
2	Conditional probability. Multiplication and total probability rules. Independence. Bayes rule.
3	Discrete random variables. Probability mass function & cumulative distribution functions. Mean anc variance.
4	Probability distributions: uniform, binomial and geometric distributions. Their mean and variance.
5	Probability distributions: negative binomial, hypergeometric and Poisson distributions.
6	Continuous random variables. Probability density functions and cumulative distribution functions. Mean and variance.
7	Continuous probability distributions. The uniform and normal distribution.
8	Midterm exam.
9	The exponential distribution. Overview of lognormal, Erlang, Gamma and Weibull probability distributions.
10	Random sampling and data description.
11	Point estimation of parameters. Unbiased estimators. Estimators’ variance, std error. Sample distribution.
12	Statistical intervals for a single sample. Confidence intervals on the mean of normal distributions.
13	Hypothesis testing. Types of statistical hypothesis, one-sided and two-sided hypothesis.
14	Tests on the mean and variance of various distributions.

Prerequisite(s):
Textbook:	D. Montgomery, G.Runger , «Applied Statistics and Probability for Engineers»
Other References:
Laboratory Work:
Computer Usage:
Others:	No

COURSE LEARNING OUTCOMES

1	To comprehend and model various topics of probabilistic uncertainty that they will encounter in engineering scenarios.
2	To understand concepts of discrete probability, conditional probability, independence, and be able to apply these concepts to engineering applications
3	To be able to calculate the distribution function of a random variables and their means, variances and standard deviations.
4	To be able to properly organize and evaluate data.
5	Precaution and arrangement ability from statistical results.
6	Problem solve ability with theoretical and statistical techniques.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Bachelor in Electronics and Digital Communication Engineering (3 years) Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	5
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	5
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	4
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	4
8	Ability for effective oral and official communication skills in foreign language.	4
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	4
10	Engineering graduates with well-structured responsibilities in profession and ethics.	4
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	4
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	2	7.5
Midterm Exam(s)	1	25
Quiz	2	7.5
Final Exam	1	40
Attendance		5
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Mid-terms	1	11	11
Assignments			0
Final examination	1	16	16
Other	1	14	14
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5