Türkçe | English FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES Course Catalog http://www.ktu.edu.tr/isbb Phone: +90 0462 +90 (462) 3773112 FENF
FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES /    IST4006 Regression Analysis 4+0+0 ECTS:6 Year / Semester Spring Semester Level of Course First Cycle Status Compulsory Department DEPARTMENT of STATISTICS and COMPUTER SCIENCES Prerequisites and co-requisites None Mode of Delivery Face to face Contact Hours 14 weeks - 4 hours of lectures per week Lecturer Prof. Dr. Zafer KÜÇÜK Co-Lecturer PROF. DR. Türkan ERBAY DALKILIÇ Language of instruction Turkish Professional practise ( internship ) None The aim of the course: The aim of this course is to analyze the data that are confronted in real life and then make students get the knowledge and skills for commenting on the analysis results.
 Learning Outcomes CTPO TOA Upon successful completion of the course, the students will be able to : LO - 1 : estimate the model parameters and obtain the most suitable models 1,2,3,4,5,6,7,8,9,10,11 1 LO - 2 : model better using statistical packed programs 1,2,3,5,6,7,8,9,11 1 LO - 3 : test the hypotheses claimed about proposed model 1,2,3,4,5,7,8,10,11 1 LO - 4 : make statistical comments about proposed model 1,2,3,4,7,8,9,10,11 1 CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome
 Contents of the Course
 Linear regression and correlation in case of single independent variable, general linear regression analysis, relations convertible into linear type, deviations from classical linear regression model, regression analysis with artificial variables, establishing the best regression model, regression concept with nonlinear parameter
 Course Syllabus Week Subject Related Notes / Files Week 1 Regression analysis: general information, content, discussing purpose and method. Week 2 Variables, the regression coefficient, data types. Week 3 Simple linear regression, least squares method, examples Week 4 Data reduction methods, model predictive, variance coefficients. Week 5 Attention control of regression coefficients , confidence intervals. Week 6 Attention control of regression coefficients, confidence intervals (continued) Week 7 Application, Creating the ANOVA table, Controlling the importance and applying significance of departure. Week 8 Mid-term exam Week 9 Correlation, the importance of control, non-linear regression model. Week 10 Introductory information for the topic, quadratic forms and distributions, the expected value. Week 11 Simple linear regression in the matrix representation, LSM. Week 12 Examples Week 13 hypothesis testing in multiple linear regression , examples. Week 14 Polynomial regression equations, interval estimation, multiple-entry correlation. Week 15 Inconsistent value, changing variability, dummy variables, multiple connections, variable selection methods. Week 16 End-of-term exam
 Textbook / Material
 1 Yan, Xin; Su, Xiaogang, 2009; Linear Regression Analysis : Theory and Computing, World Scientific Publishing Co. eBook. 349p.