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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 4019 GENERALIZED LINEAR MODELS ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR RABIA ECE OMAY

Offered to

Econometrics Econometrics (Evening)

Course Objective

This course aims to provide students with an understanding of the theory and application areas of generalized linear models and to provide students with applied skills. Thus, the student can recognize generalized linear models, construct, analyze and interpret different models for different exponential family distributions.

Learning Outcomes of the Course Unit

1   Recognizing exponential family distributions 2   To explain the basic concepts of generalized linear models 3   To recognize the characteristic structure of generalized linear models 4   Constructing and interpreting the generalized linear models 5   To understand the theoretical background of logistic regression models and to construct and interpret logistic regression models for different data sets 6   To understand the theoretical background of Poisson regression models and to construct and interpret logistic regression models for different data sets

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Basic Information 2 Maximum likelihood method 3 Maximum likelihood function for linear model 4 Exponential family distributions 5 Exponential family distributions 6 Characteristic structure of generalized linear models 7 Characteristic structure of generalized linear models 8 Maximum likelihood function for generalized linear models 9 Midterm Exam 10 Logistic regression and R applications 11 Logistic regression and R applications 12 Poisson regression and R applications 13 Poisson regression and R applications 14 GLM and R applications 15 GLM and R applications

Recomended or Required Reading

Dobson, A. J., An Introduction to Generalized Linear Regression, Chapmen&Hall.

Planned Learning Activities and Teaching Methods

Lecture, practice, group work, presentation

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60

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)

Prof. Dr. Rabia Ece OMAY (rabiaece.omay@deu.edu.tr)

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparations before/after weekly lectures 12 1 12 Preparation for midterm exam 1 20 20 Preparation for final exam 1 25 25 Independant Study 1 20 20 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 115

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 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 LO.6 1