Rapor Tarihi: 27.03.2026 02:58
| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Probability and Statistics | INS205 | Turkish | Compulsory | 3. Semester | 3 + 0 | 3.0 | 3.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | Presentation – Sampling – Discussion – Question and Answer |
| Course Coordinator | Dr. Öğr. Üyesi Adil GÜLTEKİN |
| Instructor(s) | Dr. Öğr. Üyesi Adil GÜLTEKİN (Güz) |
| Goals | The aim of this course is to introduce the fundamental concepts of probability and statistics and to develop students’ skills in data collection, analysis, and interpretation. It also aims to provide the ability to make decisions under uncertainty. Students learn how to model random events using probability theory and apply different types of distributions to engineering problems. In addition, real-world data is analyzed using statistical methods, and relationships between variables are examined through correlation and regression analysis. Sampling and estimation techniques, along with hypothesis testing, are used to support scientific decision-making processes. Ultimately, the course aims to enhance students’ analytical thinking and problem-solving skills. |
| Course Content | This course covers the importance of statistics and its fundamental concepts, along with data collection methods and the presentation of data through graphs and tables. Statistical series and different types of averages (arithmetic, geometric, quadratic, and harmonic means), including sensitive and insensitive averages, are discussed in detail. Measures of central tendency such as mode, median, and quartiles are also examined, together with the concept of index numbers, including time, spatial, fixed, and variable indices. Measures of variability such as range, mean absolute deviation, variance, and coefficient of variation are introduced, and relationships between variables are analyzed through correlation, covariance, coefficient of determination, and rank correlation. The least squares method and the concept of standard error are also included. In the probability section, the importance of probability and its basic concepts are presented, followed by counting principles, permutations, and combinations. Bayes’ theorem, sampling processes, and distributions such as normal, binomial, Poisson, and hypergeometric are explained. Finally, the course concludes with estimation, decision-making, and hypothesis testing. |
| # | Öğrenme Kazanımı |
| 1 | Students define and explain the fundamental concepts of statistics and probability. |
| 2 | Students understand data collection methods and present data appropriately (graphs, tables). |
| 3 | Students calculate and interpret measures of central tendency and variability. |
| 4 | Students perform correlation and regression analyses to evaluate relationships between variables. |
| 5 | Students model random events using probability theory. |
| 6 | Students apply sampling and estimation techniques and interpret the results. |
| 7 | Students make scientific decisions using hypothesis testing. |
| 8 | Students enhance analytical thinking and problem-solving skills through statistical methods. |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Role of statistics in engineering, data types, basic terms | |
| 2. Week | Data collection methods, presentation with tables and graphs | |
| 3. Week | Frequency tables, distribution tables, interpretation of series | |
| 4. Week | Arithmetic, geometric, quadratic, and harmonic means, sensitive/insensitive averages | |
| 5. Week | Measures of central tendency, example calculations | |
| 6. Week | Time, spatial, fixed, and variable indices | |
| 7. Week | Range, mean absolute deviation, variance, coefficient of variation | |
| 8. Week | Covariance, coefficient of determination, rank correlation | |
| 9. Week | Regression equations, prediction errors | |
| 10. Week | Random events, probability definition and rules | |
| 11. Week | Permutations and combinations | |
| 12. Week | Conditional probability, sampling methods | |
| 13. Week | Normal and binomial distributions, Poisson and hypergeometric distributions | |
| 14. Week | Parameter estimation, confidence intervals, hypothesis testing |
| Program Requirements | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 | DK7 | DK8 |
|---|
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| Güz Dönemi | |||
| Responsible Personnel | Grup | Evaluation Method | Percentage |
|---|---|---|---|
| Dr. Öğr. Üyesi Adil GÜLTEKİN | Vize | 50.00 | |
| Dr. Öğr. Üyesi Adil GÜLTEKİN | Final | 50.00 | |
| Toplam | 100.00 | ||
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 2 | 28 |
|
Ders Dışı |
Preparation, After Class Study | 14 | 2 | 28 |
| Interview | 14 | 1 | 14 | |
| Other Activities | 1 | 3.5 | 3.5 | |
|
Sınavlar |
Midterm | 1 | 1.5 | 1.5 |
| Final | 1 | 1.5 | 1.5 | |
| Total Workload | 76.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 3.0 | ||
