Istatistiksel Methods
Environmental Engineering
Institute of Natural and Applied Sciences
Second Cycle (Master's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Istatistiksel Methods | CEV501 | Compulsory | Master's degree | 1 | Fall | 8 |
Name of Lecturer(s)
Prof. Dr. Hakan PEKEY
Prof. Dr. Beyhan PEKEY
Learning Outcomes of the Course Unit
1) Explain statistical concepts
2) Describe scientific sampling methods
3) Apply statistical analysis of environmental data and comment on analysis results
4) Use appropriate statistical hypothesis tests to obtain information from research hypothesis
5) Explain the difference between parametric and non-parametric tests
6) Use the linear regression technique to analyze the relationship between different random variables
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | Low | No relation | No relation | No relation | No relation | No relation | Low | Low | High | Low | No relation | No relation | |
| 2 | Low | Low | Low | No relation | Low | No relation | Low | Low | Low | Middle | No relation | No relation | |
| 3 | Low | Low | Low | Low | Low | Low | Low | Low | Middle | Middle | No relation | No relation | |
| 4 | Low | Low | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | No relation | No relation | |
| 5 | Low | No relation | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | No relation | No relation | |
| 6 | Low | No relation | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not-existence
Course Contents
This course equips candidates with in-depth knowledge on basic statistical definitions, scientific sampling methods, the characterization of environmental data, confidence intervals, the determination of outlier data, data distribution, hypothesis testing, regression and correlation analysis, parametric or non-parametric multiple comparison tests (ANOVA).
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Simulation
6) Self Study
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
30% |
|---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required