| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Statistics | TB102 | Turkish | Compulsory | 2. Semester | 3 + 0 | 3.0 | 3.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | Face to face |
| 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 | Interview, Presentation (Preparation) |
| 2. Week | Variable types, frequency distributions, descriptive statistics for example, mode, median, | Presentation (Preparation), Interview |
| 3. Week | Change measures, the change range, standard deviation, variance | Interview, Presentation (Preparation) |
| 4. Week | The theory of probability and chance variables, event, probability, determining the number of events | Interview, Presentation (Preparation) |
| 5. Week | Discrete chance variables, continuous chance variables | Interview, Presentation (Preparation) |
| 6. Week | Probability distributions for discrete variables, binomial distributions, Poisson distributions | Interview, Presentation (Preparation) |
| 7. Week | Uniform distribution, gamma type of probability distributions, Weibull probability distribution, implementation of normal distribution to Binomial distribution | Presentation (Preparation), Interview |
| 8. Week | Midterm exam | |
| 9. Week | Statistical interpretation, statistical estimator types, point and interval estimation for the average population | Presentation (Preparation), Interview |
| 10. Week | Hypothesis testing, large and small sample tests, t distribution, the comparison of two means | Interview, Presentation (Preparation) |
| 11. Week | The chi-square distribution, confidence intervals for variance and controls for hypothesis testing, comparison of two variance and F test | Interview, Presentation (Preparation) |
| 12. Week | The relationship between variables, linear relations, examination of the regression equation | Interview, Presentation (Preparation) |
| 13. Week | Correlation, the correlation coefficient calculation, control and homogeneity testing | Presentation (Preparation) |
| 14. Week | Trial design and analysis, randomized plots and blocks, Latin square design experiment, comparing the averages and nonparametric tests and applications | Interview, Presentation (Preparation) |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Uses knowledge of natural sciences and mathematics to develop various processes in the field. | ||||||
| 2 | Demonstrates behavior in line with ethical and deontological principles in decision-making and implementation processes. | ||||||
| 3 | Applies scientific and technological developments in practices within the field. | ||||||
| 4 | Integrates basic 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 the field. | ||||||
| 6 | Applies plant and animal production processes in accordance with scientific and technical principles. | ||||||
| 7 | Uses data-oriented basic technologies of the agricultural sector in production processes. | ||||||
| 8 | Applies sustainability principles and approaches to agricultural processes. | ||||||
| 9 | Uses managerial and institutional knowledge for agriculture, taking into account global and local developments. | ||||||
| 10 | Manages the cultivation, breeding, and adaptation processes of field crops and applies sustainable agricultural principles considering biodiversity and ecological balance. | ||||||
| 11 | Manages seed standards effectively in accordance with legislation. | ||||||
| 12 | Diagnoses yield and quality problems in field crops and develops effective solutions. | ||||||
| 13 | Develops innovative decision support systems based on scientific evidence using land-based digital agriculture technologies in field farming. | ||||||
| 14 | Manages field crop production with sustainable and entrepreneurial business models in line with legal and ethical responsibilities, global policies, and market dynamics. | ||||||
| 15 | Uses effective communication and leadership skills to carry out multifaceted agricultural projects, including extension activities for farmers. | ||||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 |
|---|---|---|---|---|
| PY1 | 0 | 0 | 0 | 0 |
| PY2 | 0 | 0 | 0 | 0 |
| PY3 | 0 | 0 | 0 | 0 |
| PY4 | 0 | 0 | 0 | 0 |
| PY5 | 0 | 0 | 0 | 0 |
| PY6 | 0 | 0 | 0 | 0 |
| PY7 | 0 | 0 | 0 | 0 |
| PY8 | 0 | 0 | 0 | 0 |
| PY9 | 0 | 0 | 0 | 0 |
| PY10 | 0 | 0 | 0 | 0 |
| PY11 | 0 | 0 | 0 | 0 |
| PY12 | 0 | 0 | 0 | 0 |
| PY13 | 0 | 0 | 0 | 0 |
| PY14 | 0 | 0 | 0 | 0 |
| PY15 | 0 | 0 | 0 | 0 |
| 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 | ||
