Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
-1YBS325 Linear Models3+0+03631.01.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Management Information Systems
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The aim of this course is to provide students with a basic understanding of linear models, to enable them to model linear relationships between variables, and to interpret the results of these models correctly.
Course Content Within the scope of this course, the concept of the linear model is introduced and the modeling of linear relationships between variables is examined. In the course, linear relationships between variables are analyzed using graphical methods, and the structure of the simple linear model and the idea of estimation are explained.
Course Methods and Techniques Lectures and labs using the software R
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Research Assist.Dr. SEVCAN ÇAĞLAYAN sevcan.caglayan@gedik.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Kadir Sümbüloğlu (2007).Regresyon Yöntemleri Ve Korelasyon Analizi. Nobel Yayınevi
Introduction to Linear Regression Analysis. D.C. Montgomery, E.A. Peck, G.G. Vining), 5. Basımdan çeviri, Çeviri Editörü: Aydın Erar, Nobel Yayınevi.
Regresyon Analizi (2021). Hamza Gamgam, Bülent Altunkaynak, Seçkin Yayıncılık.

Course Category
Mathematics and Basic Sciences %10
Social Sciences %90

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Quizzes 1 % 10
Assignment 1 % 10
Final examination 1 % 50
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 10 3 30
Assignments 1 6 6
Mid-terms 1 30 30
Practice 1 3 3
Final examination 1 50 50
Total Work Load   Number of ECTS Credits 6 161

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 İstatistiksel modeli tanımlamak
2 Doğrusal modeli kurabilme, parametrelerini tahmin etmek ve bu parametrelerin anlamlılığını test etmek
4 modelin dayandığı varsayımların geçerliliğini değerlendirmek
5 Hipotez testleri ve güven aralıklarını kullanarak model sonuçlarını temel düzeyde değerlendirmek.
6 Veri setleri kullanarak R programı ile doğrusal model uygulamaları yapmak ve elde edilen sonuçları yorumlamak.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Doğrusal Modellere Giriş: Model kurma, stokastik ve deterministik ilişki,korelasyon, nedensellik, bağımlı, bağımsız değişkenler ve hata terimi, doğrusal modellere örnekler
2 Basit Doğrusal Regresyon Modeli: Model Kurma, Model Tahmini, Parametrelerin Tahmini
3 Çoklu Doğrusal Regresyon Modeli
4 Tahmin Yöntemleri
5 Çözümlü Quiz
6 Hipotez testleri: T testi, F testi, güven aralıkları, standart sapma, varyans, olasılık
7 Genel tekrar
8 Midterm Exam
9 Uygulama
10 Uyum Ölçütleri, ÇDB, Değişen Varyans, Otokorelasyon
11 Model tanımlanması, Model Seçimi
12 Çözümlü Örnekler
13 Doğrusal Modeller Uygulaması
14 Soru çözme ve genel tekrar
15 Final

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8
All 4 4 1
C1
C2
C4
C5
C6 5

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  https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=265144&curProgID=5702&lang=en