Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
MB0057		Regression Analysis	2/2/0	DE	Turkish	5

Course Goals	Regression is a widely used statistical technique for investigating the relationship between variables. This course provides a thorough treatment of the theory and practice of regression analysis. The course provides the tools required for model building, estimation, prediction and diagnostics using regression models.
Prerequisite(s)	Linear algebra
Corequisite(s)	No
Special Requisite(s)	No
Instructor(s)	Assist. Prof. Dr. Alper ÜLKER
Course Assistant(s)	Teaching Assistant Banu BAKLAN ŞEN
Schedule	Tuesday 09:00-11:00 , Wednesday 11:00-13:00 CATS Collaborate
Office Hour(s)	Wednesday 13:00-15:00
Teaching Methods and Techniques	This course will be taught using interactive lectures ( I will present material to the class but will encourage interaction and discussion during the lecture process). The focus of the course will be on practical applications rather than theory.  Application for regression analysis  Use computer lab.
Principle Sources	-
Other Sources

Course Schedules
Week	Contents	Learning Methods
1. Week	Correlation,uses of regression models	Lecture
2. Week	Simple linear regression	Lecture
3. Week	Computer Lab.(Matlab)
4. Week	Inferences from regression equations	Lecture
5. Week	Computer Lab.(Matlab)
6. Week	Multiple linear regression and its matrix formulation	Lecture
7. Week	Computer Lab.(Matlab)
8. Week	Midterm exam
9. Week	Regression diagnostics (infuential points and multicollinearity)	Lecture
10. Week	Computer Lab.(Matlab)
11. Week	Model building and variable selection	Lecture
12. Week	Special types of regression: polynomial and indica- tor variable regression,dummy variable	Lecture
13. Week	Special topics (generalized linear models, nonlinear regression)	Lecture
14. Week	Computer Lab.(Matlab)
15. Week	Final exam
16. Week	Final exam
17. Week	Final exam

Assessments
Evaluation tools	Quantity	Weight(%)
Midterm(s)	1	30
Homework / Term Projects / Presentations	1	20
Final Exam	1	50

Program Outcomes PO-1 Interpreting advanced theoretical and applied knowledge in Mathematics and Computer Science. PO-2 Critiquing and evaluating data by implementing the acquired knowledge and skills in Mathematics and Computer Science. PO-3 Recognizing, describing, and analyzing problems in Mathematics and Computer Science; producing solution proposals based on research and evidence. PO-4 Understanding the operating logic of computer and recognizing computational-based thinking using mathematics as a discipline. PO-5 Collaborating as a team-member, as well as individually, to produce solutions to problems in Mathematics and Computer Science. PO-6 Communicating in a foreign language, and interpreting oral and written communicational abilities in Turkish. PO-7 Using time effectively in inventing solutions by implementing analytical thinking. PO-8 Understanding professional ethics and responsibilities. PO-9 Having the ability to behave independently, to take initiative, and to be creative. PO-10 Understanding the importance of lifelong learning and developing professional skills continuously. PO-11 Using professional knowledge for the benefit of the society.

Learning Outcomes LO-1 Has a teoretical and applied knowledge about regression analysis. LO-2 Set linear and nonlinear model of datas LO-3 Analysis model estimated errors in a regression model. LO-4 Apply analysis technics to the regression analysis of real data. LO-5 Use Matlab effectively

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11
LO 1
LO 2
LO 3
LO 4
LO 5