| 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 | |
| Instructor(s) | |
| Goals | To ensure students learn the fundamental concepts and methods of statistics, and to inform them about the application and interpretation of these basic statistical methods |
| Course Content | Statistics, various definitions, sample and population Summarization of data and data Descriptive statistics Linear Correlation and Regression Experiment, event and probability, some probability laws Chance change and expected value Some important intermittent distributions Distributions of continuous variables Distributions of continuous variables Sampling distributions Test distributions, Statistical estimation, population averaging estimation, Estimates of some parameters and confidence intervals, Hypothesis control |
| # | Öğrenme Kazanımı |
| 1 | The student learns the fundamental concepts of statistics |
| 2 | Comprehends statistical estimation and hypothesis testing. |
| 3 | Acquires knowledge of the application of fundamental statistical methods. |
| 4 | Comprehends the interpretation of statistical outputs. |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Fundamental Concepts and Definitions | Presentation (Preparation) Interview |
| 2. Week | Variable Types, Frequency Distributions, Descriptive Sample Statistics, Mode, Median | Presentation (Preparation) Interview |
| 3. Week | Measures of Variability (or Measures of Dispersion), Range, Standard Deviation, Variance | Interview Presentation (Preparation) |
| 4. Week | Probability Theory and Random Variables, Event, Probability, Determination of the Number of Events | Presentation (Preparation) Interview |
| 5. Week | Discrete Random Variables, Continuous Random Variables | Presentation (Preparation) Interview |
| 6. Week | Probability Distributions for Discrete Variables, Binomial Distributions, Poisson Distributions | Presentation (Preparation) Interview |
| 7. Week | Uniform Distribution, Gamma-Type Probability Distributions, Weibull Probability Distribution, Application of Normal Distribution Approximation to the Binomial Distribution" | Interview Presentation (Preparation) |
| 8. Week | Uniform Distribution, Gamma-Type Probability Distributions, Weibull Probability Distribution, Application of Normal Distribution Approximation to the Binomial Distribution | Interview Presentation (Preparation) |
| 9. Week | Statistical Interpretation, Types of Statistical Estimators, Point and Interval Estimation for Population Mean. | Presentation (Preparation) Interview |
| 10. Week | Hypothesis Testing, Large and Small Sample Tests, t-Distribution, Comparison of Two Means | Presentation (Preparation) Interview |
| 11. Week | Chi-square Distribution, Confidence Interval and Hypothesis Tests for Variance, Comparison of Two Variances, and the F-Test | Interview Presentation (Preparation) |
| 12. Week | Relationships Between Variables, Linear Relationships, Examination of the Regression Equation | Presentation (Preparation) Interview |
| 13. Week | Correlation, Calculation of the Correlation Coefficient, Control (or Verification), and Test of Homogeneity | Presentation (Preparation) Interview |
| 14. Week | Experimental Designs and Analyses, Completely Randomized Designs (CRD) and Randomized Block Designs (RBD), Latin Square Design, Comparison of Means, and Non-Parametric Tests and their Applications. | Presentation (Preparation) Interview |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Utilizes (or Applies) knowledge of natural sciences and mathematics in developing various processes in their field. | ✔ | |||||
| 2 | Demonstrates adherence (or behaves) to ethical and deontological principles in decision-making and implementation processes. | ✔ | |||||
| 3 | Utilizes (or Applies) scientific and technological developments in the applications within their field. | ✔ | |||||
| 4 | Integrates (or Combines) fundamental engineering knowledge with technical tools to solve engineering problems in their field using an analytical approach. | ✔ | |||||
| 5 | Designs all technical systems, system components, and production processes relevant to their field. | ✔ | |||||
| 6 | Implements (or Applies) plant and animal production processes in accordance with scientific and technical principles. | ✔ | |||||
| 7 | Utilizes (or Employs) data-driven core technologies in agricultural production processes. | ✔ | |||||
| 8 | Applies (or Implements) sustainability principles and approaches to agricultural processes. | ✔ | |||||
| 9 | Utilizes (or Applies) managerial and institutional knowledge related to agriculture, while considering (or observing) global and local developments. | ✔ | |||||
| 10 | Manages soil and water resources and agricultural waste sustainably by integrating scientifically based irrigation, drainage, and soil conservation systems with precision agriculture and digital water management technologies. | ✔ | |||||
| 11 | Designs agricultural machinery and equipment for agricultural production and post-harvest processes, evaluates their performance, and enhances their efficiency through automation. | ✔ | |||||
| 12 | Develops functional and environmentally sensitive (or sustainable) solutions in the design of agricultural structures (such as greenhouses, barns, and pens) by utilizing modern engineering and construction technologies. | ✔ | |||||
| 13 | Analyzes energy efficiency for agriculture and develops effective systems by integrating biofuel production and other sustainable energy sources | ✔ | |||||
| 14 | Analyzes precision agriculture data (such as satellite imagery, unmanned aerial vehicles (UAVs), and handheld radiometers) to develop and implement systems that optimize resource management. | ✔ | |||||
| 15 | Executes entrepreneurial projects developed based on legal and ethical boundaries by following current developments, manages them through interdisciplinary collaboration, and transfers the acquired knowledge to stakeholders. | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 |
|---|---|---|---|---|
| PY1 | 1 | 1 | 1 | 1 |
| PY2 | 1 | 1 | 1 | 1 |
| PY3 | 1 | 1 | 1 | 1 |
| PY4 | 5 | 5 | 5 | 5 |
| PY5 | 1 | 1 | 1 | 1 |
| PY6 | 1 | 1 | 1 | 1 |
| PY7 | 1 | 1 | 1 | 1 |
| PY8 | 1 | 1 | 1 | 1 |
| PY9 | 1 | 1 | 1 | 1 |
| PY10 | 1 | 1 | 1 | 1 |
| 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 |
|
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 3 | 42 |
|
Ders Dışı |
Other Activities | 14 | 2 | 28 |
|
Sınavlar |
Midterm 1 | 1 | 1 | 1 |
| Homework 1 | 1 | 4.5 | 4.5 | |
| Final | 1 | 1 | 1 | |
| Total Workload | 76.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 3.0 | ||
