Rapor Tarihi: 15.01.2026 09:48
| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Statistics | ZFZ102 | Turkish | Compulsory | 2. Semester | 3 + 0 | 3.0 | 3.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | Face to face and through projections slide, texts and using additional resources such as book |
| Course Coordinator | |
| Instructor(s) | |
| Goals | Student enables information about the basic statistical concepts and methods and they learn implementation and interpretation of these basic statistical methods. |
| Course Content | Indroduction to statistical analyses, general terms, presentation of data and summarization, identifcate statistics, probability and their distrubitions (binomial, poissonial and normal), statistical comments, hipothesis, analyses of the regression and correlations, analyses of the data. |
| # | Öğrenme Kazanımı |
| 1 | Students learn the basic concepts of statistics |
| 2 | Establish statistical estimation and hypothesis |
| 3 | Understand to application of basic statistical methods |
| 4 | Learn to evaluation of statistical outputs |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Basic terms and definitions | |
| 2. Week | Variable types, frequency distributions, descriptive statistics for example, mode, median, | |
| 3. Week | Change measures, the change range, standard deviation, variance | |
| 4. Week | The theory of probability and chance variables, event, probability, determining the number of events | |
| 5. Week | Discrete chance variables, continuous chance variables | |
| 6. Week | Probability distributions for discrete variables, binomial distributions, Poisson distributions | |
| 7. Week | Uniform distribution, gamma type of probability distributions, Weibull probability distribution, implementation of normal distribution to Binomial distribution | |
| 8. Week | Midterm exam | |
| 9. Week | Statistical interpretation, statistical estimator types, point and interval estimation for the average population | |
| 10. Week | Hypothesis testing, large and small sample tests, t distribution, the comparison of two means | |
| 11. Week | The chi-square distribution, confidence intervals for variance and controls for hypothesis testing, comparison of two variance and F test | |
| 12. Week | The relationship between variables, linear relations, examination of the regression equation | |
| 13. Week | Correlation, the correlation coefficient calculation, control and homogeneity testing | |
| 14. Week | Trial design and analysis, randomized plots and blocks, Latin square design experiment, comparing the averages |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Uses science and math knowledge to develop various processes in the field. | ✔ | |||||
| 2 | Demonstrates behavior consistent with ethical and deontological principles in decision-making and implementation processes. | ✔ | |||||
| 3 | It is used in applications in the field of scientific and technological developments. | ✔ | |||||
| 4 | Integrates fundamental engineering knowledge with technical tools to solve engineering problems in the field using an analytical approach. | ✔ | |||||
| 5 | Designs all technical systems, system components, and production processes related to their field. | ✔ | |||||
| 6 | It implements plant and animal production processes in accordance with scientific and technical principles. | ✔ | |||||
| 7 | It uses data-driven core technologies in the agricultural sector in production processes. | ✔ | |||||
| 8 | Applies sustainability principles and approaches to agricultural processes. | ✔ | |||||
| 9 | Uses administrative and institutional information related to agriculture, taking into account global and local developments. | ✔ | |||||
| 10 | By analyzing the life dynamics of plant diseases, harmful organisms, and weeds, it develops innovative solutions to these problems. | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 |
|---|---|---|---|---|
| PY1 | 4 | 4 | 4 | 4 |
| PY2 | 4 | 4 | 4 | 4 |
| PY3 | 4 | 4 | 4 | 4 |
| PY4 | 4 | 4 | 4 | 4 |
| PY5 | 4 | 4 | 4 | 4 |
| PY6 | 4 | 4 | 4 | 4 |
| PY7 | 3 | 3 | 3 | 3 |
| PY8 | 4 | 4 | 4 | 4 |
| PY9 | 4 | 4 | 4 | 4 |
| PY10 | 4 | 4 | 4 | 4 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 3 | 42 |
|
Ders Dışı |
Preparation, After Class Study | 10 | 1 | 10 |
|
Sınavlar |
Midterm 1 | 1 | 20 | 20 |
| Final | 1 | 30 | 30 | |
| Total Workload | 102 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 3.0 | ||
