Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty of Science
  • Department of Statistics
  • Course Structure Diagram with Credits
  • Regression Analysis
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title İST333 - Regression Analysis
Course Type Required Courses
Language of Instruction Türkçe
Laboratory + Practice 4+0
ECTS 6.0
Course Instructor(s) PROFESÖR DOKTOR YELİZ MERT KANTAR
Mode of Delivery The mode of delivery of this course is Face to face
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended There is no recommended optional programme component for this course.
Recommended Reading List Kleinbaum, D. G., Kupper, L. L., Muller, K. E., Applied Regression Analysis and Other Multivariate Methods, 3rd Edition, Duxbury Press, 1998.
Assessment methods and criteria 2 midterms 1 final exam
Work Placement Not suitable for this course.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Conditional Expectation and Regression
Week - 2 Ordinary Least Squares (LSM) Estimators with Simple Linear Regression Models and Parameters
Week - 3 Assumptions of Linear Regression; Properties of the OLS Estimators
Week - 4 Gauss-Markov Theorem
Week - 5 Hypothesis Testing and Confidence Intervals in Simple Linear Regression
Week - 6 Coefficient of Determination
Week - 7 Multiple Linear Regression Model in Matrix Notation
Week - 8 Hypothesis Testing and Confidence Intervals in Multiple Linear Regression
Week - 9 Dummy Variable
Week - 10 Checking of Assumptions (Residual Analysis)
Week - 11 Multicollinearity
Week - 12 Selection of variable
Week - 13 Heteroscedasticity and Autocorrelation
Week - 14 Applications

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Drill - Practise
  • Problem Solving
  • Competences
  • Rational
  • Creative
  • Follow ethical and moral rules
  • Use time effectively
  • Abstract analysis and synthesis
  • Problem solving
  • Information Management
  • Elementary computing skills
  • Decision making

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 20
2.Midterm Exam 1 20
Homework 1 10
Final Exam 1 50
Toplam (%) 100
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