Rapor Tarihi: 27.03.2026 01:41
| 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 |
| Course Coordinator | Öğr. Gör. Canberk BATU |
| Instructor(s) | Öğr. Gör. Canberk BATU (Bahar) |
| 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 | Preparation, After Class Study, Presentation (Preparation) |
| 2. Week | Variable types, frequency distributions, descriptive statistics for example, mode, median | Preparation, After Class Study, Presentation (Preparation), Practice |
| 3. Week | Change measures, the change range, standard deviation, variance | Preparation, After Class Study, Presentation (Preparation), Practice |
| 4. Week | The theory of probability and chance variables, event, probability, determining the number of events | Preparation, After Class Study, Presentation (Preparation), Practice |
| 5. Week | Discrete chance variables, continuous chance variables | Interview, Presentation (Preparation) |
| 6. Week | Probability distributions for discrete variables, binomial distributions, Poisson distributions | Preparation, After Class Study, Presentation (Preparation), Practice |
| 7. Week | Uniform distribution, gamma type of probability distributions, Weibull probability distribution, implementation of normal distribution to Binomial distribution | Preparation, After Class Study, Presentation (Preparation), Practice |
| 8. Week | Uniform distribution, gamma type of probability distributions, Weibull probability distribution, implementation of normal distribution to Binomial distribution | Preparation, After Class Study, Presentation (Preparation), Practice |
| 9. Week | Statistical interpretation, statistical estimator types, point and interval estimation for the average population | Interview, Presentation (Preparation) |
| 10. Week | Hypothesis testing, large and small sample tests, t distribution, the comparison of two means | Preparation, After Class Study, Presentation (Preparation), Practice |
| 11. Week | The chi-square distribution, confidence intervals for variance and controls for hypothesis testing, comparison of two variance and F test | Preparation, After Class Study, Presentation (Preparation), Practice |
| 12. Week | The relationship between variables, linear relations, examination of the regression equation | Preparation, After Class Study, Presentation (Preparation), Practice |
| 13. Week | Correlation, the correlation coefficient calculation, control and homogeneity testing | Preparation, After Class Study, Presentation (Preparation), Practice |
| 14. Week | Trial design and analysis, randomized plots and blocks, Latin square design experiment, comparing the averages | Preparation, After Class Study, Presentation (Preparation) |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | .Applies knowledge of natural sciences and mathematics to the development of various processes within the field. | ✔ | |||||
| 2 | Acts in accordance with ethical and deontological principles in decision-making and implementation processes. | ✔ | |||||
| 3 | Utilizes scientific and technological developments in field-related applications. | ✔ | |||||
| 4 | Solves engineering problems within the field through an analytical approach by integrating fundamental engineering knowledge with technical tools. | ✔ | |||||
| 5 | Designs all technical systems, system components, and production processes related to the field. | ✔ | |||||
| 6 | Implements crop and livestock production processes in accordance with scientific and technical principles. | ✔ | |||||
| 7 | Utilizes data-driven core technologies within the agricultural sector in production processes. | ✔ | |||||
| 8 | Applies sustainability principles and approaches to agricultural processes. | ✔ | |||||
| 9 | Utilizes managerial and institutional knowledge for agriculture, taking into account global and local developments. | ✔ | |||||
| 10 | Integrates fundamental scientific knowledge in the fields of genetics, molecular biology, microbiology, and biochemistry into agricultural biotechnology processes through a critical approach. | ✔ | |||||
| 11 | Produces innovative and sustainable biotechnological solutions to agricultural problems by effectively utilizing laboratory and field applications. | ✔ | |||||
| 12 | Effectively utilizes statistical, mathematical, and bioinformatic tools to analyze biological data. | ✔ | |||||
| 13 | Fulfills professional and social responsibilities by mastering the ethical, legal, intellectual property, and biosafety dimensions of biotechnological applications. | ✔ | |||||
| 14 | Effectively shares project findings obtained by working efficiently in interdisciplinary projects using effective presentation techniques. | ✔ | |||||
| 15 | Demonstrates lifelong learning and entrepreneurship skills by generating innovative ideas and continuously following scientific and technological developments in the field. | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 |
|---|---|---|---|---|
| PY1 | 3 | 3 | 3 | 3 |
| PY2 | 4 | 4 | 4 | 4 |
| PY3 | 4 | 4 | 4 | 4 |
| PY4 | 3 | 3 | 3 | 3 |
| PY5 | 3 | 3 | 3 | 3 |
| PY6 | 3 | 3 | 3 | 3 |
| PY7 | 2 | 2 | 2 | 2 |
| PY8 | 2 | 2 | 2 | 2 |
| PY9 | 2 | 2 | 2 | 2 |
| PY10 | 3 | 3 | 3 | 3 |
| PY11 | 1 | 1 | 1 | 1 |
| PY12 | 1 | 1 | 1 | 1 |
| PY13 | 1 | 1 | 1 | 1 |
| PY14 | 1 | 1 | 1 | 1 |
| PY15 | 1 | 1 | 1 | 1 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| Bahar Dönemi | |||
| Responsible Personnel | Grup | Evaluation Method | Percentage |
|---|---|---|---|
| Öğr. Gör. Canberk BATU | Vize | 30.00 | |
| Öğr. Gör. Canberk BATU | Final | 70.00 | |
| Toplam | 100.00 | ||
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 3 | 42 |
|
Ders Dışı |
Research | 14 | 1 | 14 |
| Presentation (Preparation) | 14 | 1 | 14 | |
| Practice | 1 | 4.5 | 4.5 | |
|
Sınavlar |
Midterm | 1 | 1 | 1 |
| Final | 1 | 1 | 1 | |
| Total Workload | 76.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 3.0 | ||
