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About the Programme Academic Staff Program Qualifications Lessons Matrix of Course & Program Qualifications Turkish Qualifications Framework (TQF) TQF & Program Qualifications
  1. Graduate School
  2. Department of Business Administration
  3. PhD in Business Administration
  4. PhD in Quantitative Methods
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  6. Advanced Regression Techniques
  7. Description
  • Description
  • Learning Outcomes
  • ECTS Credit Load
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Last Updated: 30/09/2026

Code - Course Title Course Type Laboratory + Practice ECTS
SAY607 - Advanced Regression Techniques I. SEMESTER 3 +0 7.5
Language of Instruction Türkçe
Course Type Elective Courses
Course Instructor(s) PROF. DR. HÜSEYİN GÜRBÜZ
Mode of Delivery The mode of delivery of this course is face to face
Place of Delivery
Used Educational Platforms
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended There is no recommended optional programme component for this course.
Recommended Resources Şıklar, E. (2000). Regresyon analizine giriş. Eskişehir: Anadolu ÜniversitesiAydın, D. (2014). Uygulamalı regresyon analizi. Ankara: Nobel
Work Placement N/A
Aims of the Course

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 Description of Regression Analysis
Week - 2 Assumptions of Linear Regression Model
Week - 3 Simple Linear Regression
Week - 4 Multiple Linear Regression
Week - 5 Assessment of Goodness of Fit
Week - 6 Residual Analysis
Week - 7 Outliers Analysis
Week - 8 Dummy Variables in Regression Analysis
Week - 9 Problems of Heteroscedasticity and Autocorrelation
Week - 10 Variable Selection and Model Development
Week - 11 Biased Estimation Methods
Week - 12 Ridge Regresyon
Week - 13 Principle Component Regression
Week - 14 Logistic Regression

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Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100

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