COURSE UNIT TITLE

: GENERALIZED LINEAR MODELS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5106 GENERALIZED LINEAR MODELS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR AYLIN ALIN

Offered to

Statistics
Statistics
STATISTICS

Course Objective

With this course the students will learn components of the generalized linear models,the estimation algorithm used for generalized linear models, and the most known models in statistics which belong this family and their properties.

Learning Outcomes of the Course Unit

1   Distinguishing the components of generalized linear models.
2   Obtaining maximum likelihood estimators for this family.
3   Measuring the goodness of fit for these models.
4   Building the models for continuous data
5   Building the models for binary data
6   Building the models for polytomuous data

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Process in model fitting; The components of a generalized linear models
2 Measuring the goodness of fit; Residuals
3 An algorithm for fitting generalized linear models
4 Error structure and Systematic component for models for continuous data with constant variance
5 Model formulae and aliasing for models for continuous data with constant variance
6 Estimation for models for continuous data with constant variance; tables as data, Homework 1
7 Algorithms for least squares for models for continuous data with constant variance
8 Selection of covariates for models for continuous data with constant variance
9 Introduction for binary data; Binomial distribution; Models for binary responses, Homework 2
10 Likelihood function; Over dispersion; examples
11 Introduction Models for polytomous data; Measurement scales
12 Multinomial distribution
13 Likelihood functions; Over dispersion, Homework 3
14 Examples

Recomended or Required Reading

Textbook:
McCullagh P, Nelder J. A. , Generalized Linear Models, 2nd ed.,.Chapman and Hall, CRC, 1989.

Planned Learning Activities and Teaching Methods

Lecture, Homeworks

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG 1 ASSIGNMENT 1
2 ASG 2 ASSIGNMENT 2
3 ASG 3 ASSIGNMENT 3
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE ASG 1 + ASG 2 + ASG 3/3 * 0.40 + FIN * 0.60
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) ASG 1 + ASG 2 + ASG 3/3 * 0.40 + RST * 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homework assignments and final exam.

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: aylin.alin @deu.edu.tr
Tel: 0232 301 85 72

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 13 3 39
Preparation for final exam 1 36 36
Preparing assignments 3 25 75
Final 1 2 2
TOTAL WORKLOAD (hours) 194

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15555
LO.25555
LO.35555
LO.455555
LO.555555
LO.655555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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