• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6111 GENERALIZED LINEER MODELS ELECTIVE 3 0 0 7

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR RABIA ECE OMAY

Offered to

Econometrics

Course Objective

The aim of the course is to develop students' ability to analyze generalized linear models and to interpret their results, and to enable them to form the statistical basis of their studies.

Learning Outcomes of the Course Unit

1   At the end of this course, the student will be able to recognize generalized linear model and construct a generalized linear model. 2   At the end of this course, the student will be able to analyze statistical analysis of a generalized linear model. 3   At the end of this course, the student will be able to construct and evaluate the logistic regression model. 4   At the end of this course, the student will be able to construct and evaluate the Poisson regression model. 5   At the end of this course, the student will be able to construct generalized linear model, logistic regression model and poisson regression model using R program and evaluate these models.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 1 Distributions arelated to normal distribution, quadratic forms, maximum likelihood estimator 2 2 Exponential family of distributions 3 3 Properties of exponential family distributions 4 4 Generalized linear models, canonical link functions 5 5 Estimation for generalized linear models, iteratively reweighted least squares 6 6 Estimation for generalized linear models 7 7 Interpretation for generalized linear models 8 8 Interpretation for generalized linear models 9 9 Logistic regression model 10 10 Poisson regression model 11 11 R applications for generalized linear models 12 12 R applications for generalized linear models 13 13 R applications for generalized linear models 14 14 R applications for generalized linear models

Recomended or Required Reading

McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, Vol. 37 of Monographs on Statistics and Applied Probability, 2 edn, Chapman and Hall, London Dobson A.J., Barnett A.G. (2018), An Introduction to Generalized Linear Models, Chapman and Hall/CRC.

Planned Learning Activities and Teaching Methods

1. Lecture 2. Show with applications 3. Discussion and analysis of identified cases

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.20 + STT * 0.30 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.30 + RST* 0.50 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) TOTAL WORKLOAD (hours) 0

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 LO.1 2 2 2 2 2 3 3 4 3 LO.2 4 4 4 4 4 3 2 3 2 LO.3 4 4 3 3 3 3 4 4 4 LO.4 4 4 4 4 4 4 4 4 4 LO.5 4 4 4 3 3 4 3 3 3