Regression analysis is a widely used suite of analytical techniques particularly suited to natural resources data.
Prerequisite(s)
None.
Corequisite(s)
None.
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Assist. Prof. Dr. Hikmet ÇAĞLAR
Course Assistant(s)
None.
Schedule
Will be announced in the forthcoming term.
Office Hour(s)
Yrd. Doç. Dr. Hikmet ÇAĞLAR, AK/3-A-09.
Teaching Methods and Techniques
Lecture and Homeworks.
Principle Sources
Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey, Introduction to Linear Regression Analysis,Wiley Series in Probability and Statistics, 5th Edition.
Other Sources
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Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to the class and the software matlab
Lecture
2. Week
Examining data and introduction to statistical models
Lecture
3. Week
Introduction to regression and transforming data
Lecture
4. Week
Linear least-squares regression
Lecture
5. Week
Multiple Regression
Lecture
6. Week
Statistical inference for regression
Lecture
7. Week
Statistical inference for regression
Lecture
8. Week
Midterm Exam
Midterm Exam
9. Week
Dummy-variable regression
Lecture
10. Week
Logistic regression
Lecture
11. Week
Model selection
Lecture
12. Week
Unusual and influential data
Lecture
13. Week
Nonlinearity
Lecture
14. Week
Collinearity and variable selection
Lecture
15. Week
Final exam Week
Final Exam
16. Week
Final exam Week
Final Exam
17. Week
Final exam Week
Final Exam
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
50
Final Exam
1
50
Program Outcomes
PO-1
Have scientific research in mathematics and computer science in the level of theoretical and practical knowledge.
PO-2
On the basis of undergraduate level qualifications, develop and deepen the same or a different areas of information at the level of expertise, and analyze and interpret by using statistical methods
PO-3
Develop new strategic approaches for the solution of complex problems encountered in applications related to the field and unforeseen and take responsibility for the solution.
PO-4
Evaluate critically skills acquired in the field of information in the level of expertise and assess the learning guides.
PO-5
Transfer current developments in the field and their work to the groups inside and outside the area supporting with quantitative and qualitative datas as written, verbal and visual by a systematic way.
PO-6
Use information and communication technologies with computer software in advanced level.
PO-7
Develop efficient algorithms by modeling problems faced in the field and solve such problems by using actual programming languages.
PO-8
Respect to social, scientific, cultural and ethical values at the stages of data collection related to the field, interpretation, and implementation.
PO-9
To solve problems related to the field, establish functional interacts by using strategic decision making processes.
PO-10
Establish and discuss in written, oral and visual communication in an advanced level by using at least one foreign language.
Learning Outcomes
LO-1
Set up models for simple linear
LO-2
Set up models for multiple regression.
LO-3
Carry out inferences for the model parameters
LO-4
Construct prediction intervals and confidence intervals
