Rapor Tarihi: 13.04.2026 03:09
| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Statistics | ENM208 | Turkish | Compulsory | 4. Semester | 3 + 0 | 3.0 | 5.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | face to face in the classroom |
| Course Coordinator | Dr. Öğr. Üyesi Mustafa İsa DOĞAN |
| Instructor(s) | Dr. Öğr. Üyesi Mustafa İsa DOĞAN (Bahar) |
| Goals | To teach the basic statistical models required to collect, analyze and interpret the data, to provide the student with the ability to collect, present data, make decisions based on the data, solve problems, design products and processes. |
| Course Content | data science, sampling, estimation, hypothesis testing, regression analysis |
| # | Öğrenme Kazanımı |
| 1 | Understanding random events in production and service systems |
| 2 | Gaining the ability to statistically predict and improve product or system performance |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Sampling distributions | Interview, Presentation (Preparation), Practice |
| 2. Week | Sampling distributions | Interview, Presentation (Preparation), Practice |
| 3. Week | Data definitions | Interview, Presentation (Preparation), Practice |
| 4. Week | estimation methods | Interview, Presentation (Preparation), Practice |
| 5. Week | estimation methods | Interview, Presentation (Preparation), Practice |
| 6. Week | Hypothesis Testing | Interview, Presentation (Preparation), Practice |
| 7. Week | Hypothesis Testing | Interview, Presentation (Preparation), Practice |
| 8. Week | Hypothesis Testing | Interview, Presentation (Preparation), Practice |
| 9. Week | Hypothesis Testing and correlation | Interview, Presentation (Preparation), Practice |
| 10. Week | regression analysis | Interview, Presentation (Preparation), Practice |
| 11. Week | regression analysis | Interview, Presentation (Preparation), Practice |
| 12. Week | variance analysis | Interview, Presentation (Preparation), Practice |
| 13. Week | data collecting | Interview, Presentation (Preparation) |
| 14. Week | nonparametric statistics | Interview, Presentation (Preparation), Practice |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | To have theoretical and / or practical knowledge in the field of mathematics, science, social sciences, engineering and / or industrial engineering, and the ability to use this knowledge to model and solve engineering problems | ✔ | |||||
| 2 | Gaining the ability to work actively in projects and projects aimed at professional development in both individual and multidisciplinary groups and taking responsibility in situations that may arise in this process | ✔ | |||||
| 5 | The ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; the ability to apply modern design methods for this purpose. | ✔ | |||||
| 6 | Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics. | ✔ | |||||
| 7 | Ability to select and use modern techniques and tools necessary for the identification, formulation, analysis and solution of complex problems encountered in engineering applications; ability to use information technologies effectively. | ✔ | |||||
| 8 | Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development. | ✔ | |||||
| Program Requirements | DK1 | DK2 |
|---|---|---|
| PY1 | 5 | 5 |
| PY2 | 4 | 4 |
| PY5 | 5 | 5 |
| PY6 | 4 | 4 |
| PY7 | 5 | 5 |
| PY8 | 2 | 2 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Sınavlar |
Midterm | 1 | 40.5 | 40.5 |
| Final | 1 | 45 | 45 | |
| Practice | 14 | 3 | 42 | |
| Total Workload | 127.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 5.0 | ||
