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IST4006Regression Analysis4+0+0ECTS:6
Year / SemesterSpring Semester
Level of CourseFirst Cycle
Status Compulsory
DepartmentDEPARTMENT of STATISTICS and COMPUTER SCIENCES
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 4 hours of lectures per week
LecturerArş. Gör. Buğra Kaan TİRYAKİ
Co-LecturerPROF. DR. Türkan ERBAY DALKILIÇ
Language of instructionTurkish
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 OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : estimate the model parameters and obtain the most suitable models1,5,8,111,
LO - 2 : model better using statistical packed programs1,5,8,111
LO - 3 : test the hypotheses claimed about proposed model1,5,8,111
LO - 4 : make statistical comments about proposed model1,5,8,111
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
 WeekSubjectRelated Notes / Files
 Week 1Regression analysis: general information, content, discussing purpose and method.
 Week 2Variables, the regression coefficient, data types.
 Week 3Simple linear regression, least squares method, examples
 Week 4Data reduction methods, model predictive, variance coefficients.
 Week 5Attention control of regression coefficients , confidence intervals.
 Week 6Attention control of regression coefficients, confidence intervals (continued)
 Week 7Application, Creating the ANOVA table, Controlling the importance and applying significance of departure.
 Week 8Mid-term exam
 Week 9Correlation, the importance of control, non-linear regression model.
 Week 10Introductory information for the topic, quadratic forms and distributions, the expected value.
 Week 11Simple linear regression in the matrix representation, LSM.
 Week 12Examples
 Week 13hypothesis testing in multiple linear regression , examples.
 Week 14Polynomial regression equations, interval estimation, multiple-entry correlation.
 Week 15Inconsistent value, changing variability, dummy variables, multiple connections, variable selection methods.
 Week 16End-of-term exam
 
Textbook / Material
1Gamgam, H., Altunkaynak B. 2021; Regresyon Analizi, Seçkin Yayıncılık, Ankara
 
Recommended Reading
1Yan, Xin; Su, Xiaogang, 2009; Linear Regression Analysis : Theory and Computing, World Scientific Publishing Co. eBook. 349p.
2Öztürkcan, Meriç. 2009; Regresyon analizi, Maltepe Üniversitesi Yayınları
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 16/04/2022 1,5 50
End-of-term exam 16 06/06/2022 1,5 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 4 14 56
Sınıf dışı çalışma 2 14 28
Laboratuar çalışması 0 0 0
Arasınav için hazırlık 12 1 12
Arasınav 1.5 1 1.5
Uygulama 0 0 0
Ödev 5 7 35
Proje 0 0 0
Dönem sonu sınavı için hazırlık 15 1 15
Dönem sonu sınavı 1.5 1 1.5
Diğer 1 0 0 0
Diğer 2 0 0 0
Total work load149