Course Information

Course Information
Course Title Code Language Type Semester L+U Hour Credits ECTS
Probability and Statistics EEM267 Turkish Compulsory 3. Semester 3 + 0 3.0 5.0
Prerequisite Courses
Course Level Undergraduate
Mode of delivery Face-to-face and online
Course Coordinator Prof. Dr. Selman KULAÇ, Dr. Öğr. Üyesi OSMAN DİKMEN, Arş. Gör. Elif Eda TAKGİL
Instructor(s) Dr. Öğr. Üyesi OSMAN DİKMEN (Güz)
Goals To enable students to learn probabilistic and statistical concepts that they can use in electrical-electronic system analysis and design, especially communication systems. In addition, relevant current issues are researched and presented in the classroom environment.
Course Content 1. Introduction to probability 2. Discrete and continuous random variables 3. Two-dimensional distributions 4. Short introduction 5. Statistical hypothesis testing and linear models 6. Regression and Analysis of Variance
Learning Outcomes
# Öğrenme Kazanımı
1 To learn basic probability concepts
2 To have knowledge about random variables and probability functions
3 To have knowledge about discrete and continous distributions
4 To learn basic statistical concepts
5 To have knowledge about statistical analysis methods
Lesson Plan (Weekly Topics)
Week Topics/Applications Method
1. Week Introduction to probability, sample space and event, joint event, probability axioms
2. Week Conditional probability, tree diagrams, total probability formula, Bayes' theorem, independence
3. Week Distribution and density functions of continuous and discrete random variables, mean and standard deviation values
4. Week Moment concept, relationship between moments, Chebyshev inequality
5. Week Bivariate (joint) discrete and continuous random variables
6. Week Conditional probability distributions, sample problems
7. Week Concepts of covariance, correlation and correlation coefficient
8. Week Solving questions
9. Week Discrete distributions: bernoulli, binomial, negative binomial, geometric, poisson and hypergeometric
10. Week Continuous distributions: uıniform, exponential, normal distributions
11. Week Sampling, statistical estimation, confidence intervals
12. Week Hypothesis testing, power of test, independence test
13. Week Regression analysis: linear regression, least squares method, multiple regression
14. Week Variance analysis
*Midterm and final exam dates are not specified in the 14-week course operation plan. Midterm and final exam dates are held on the dates specified in the academic calendar with the decision of the University Senate.
The Matrix for Course & Program Learning Outcomes
No Program Requirements Level of Contribution
1 2 3 4 5
1 Adequate knowledge in mathematics, science, and related engineering disciplines; ability to use theoretical and applied information in these areas to solve complex engineering problems.
2 Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3 Ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4 Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively.
5 Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics.
6 Ability to work effectively in disciplinary and multidisciplinary teams; ability to work individually.
7 Ability to communicate effectively both orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8 Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology, and to constantly renew oneself.
9 Knowledge about behaving by ethical principles, professional and ethical responsibility, and standards used in engineering practices.
10 Knowledge of business life practices such as project management, risk management, and change management; awareness of entrepreneurship, and innovation; knowledge of sustainable development.
11 Knowledge about the global and societal effects of engineering practices on health, environment, and safety and contemporary issues reflected in the field of engineering; awareness of the legal consequences of engineering solutions.
Relations with Education Attainment Program Course Competencies
Program Requirements DK1 DK2 DK3 DK4 DK5
PY1 4 4 4 4 4
PY2 4 4 4 4 4
PY3 3 3 3 3 3
PY4 3 3 3 3 3
PY5 3 3 3 3 3
PY6 2 2 2 2 2
PY7 2 2 2 2 2
PY8 2 2 2 2 2
PY9 0 0 0 0 0
PY10 0 0 0 0 0
PY11 0 0 0 0 0
Recommended Sources
Ders Kitabı veya Notu Ders Kitabı veya Ders Notu bulunmamaktadır.
Diğer Kaynaklar
  • 1. Prof. Dr. Fikri Akdeniz, Olasılık ve İstatistik, 2016, Akademisyen Kitabevi
  • 2. Prof. Dr. Aydın ÜSTÜN, Olasılık ve İstatistik, 2014 (Taslak)
  • 3. Stark, H., Woods J. W., Probabiliy and Random Processes with Applications to Signal Processing, Third edition, Prentice Hall, 2002
  • 4. Papoulis A., Pillai S.U., Probability, Random Variables and Stochastic Processes, Fourth edition, McGraw Hill, 2002.
ECTS credits and course workload
ECTS credits and course workload Quantity Duration (Hour) Total Workload (Hour)
Sınavlar
Midterm 1 1 25 25
Homework 1 1 15 15
Final 1 25 25
Practice 1 10 10
Practice End-Of-Term 1 10.5 10.5
Classroom Activities 14 3 42
Total Workload 127.5
*AKTS = (Total Workload) / 25,5 ECTS Credit of the Course 5.0