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| 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 | Dr. Öğr. Üyesi Buğra Kaan TİRYAKİ | | 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 | 1,5,8,11 | 1, | | LO - 2 : | model better using statistical packed programs | 1,5,8,11 | 1 | | LO - 3 : | test the hypotheses claimed about proposed model | 1,5,8,11 | 1 | | LO - 4 : | make statistical comments about proposed model | 1,5,8,11 | 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 | | |
| 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 |
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| 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 | | | |
| 1 | Gamgam, H., Altunkaynak B. 2021; Regresyon Analizi, Seçkin Yayıncılık, Ankara | | | |
| 1 | Yan, Xin; Su, Xiaogang, 2009; Linear Regression Analysis : Theory and Computing, World Scientific Publishing Co. eBook. 349p. | | | 2 | Öztürkcan, Meriç. 2009; Regresyon analizi, Maltepe Üniversitesi Yayınları | | | |
| Method of Assessment | | Type of assessment | Week No | Date | 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 work | Duration (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 load | | | 149 |
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