GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
DISCRETE MULTIVARIATE ANALYSIS/6281303
 Course Title: DISCRETE MULTIVARIATE ANALYSIS Credits 3 ECTS 7.5 Course Semester 1 Type of The Course Elective
COURSE INFORMATION
-- (CATALOG CONTENT)
-- (TEXTBOOK)
-- (SUPPLEMENTARY TEXTBOOK)
-- (PREREQUISITES AND CO-REQUISITES)
-- LANGUAGE OF INSTRUCTION
Turkish
-- COURSE OBJECTIVES
-- COURSE LEARNING OUTCOMES
Grasping the other disciplinary interaction related to his field.
The ability to use in other field the expert-level theoretical and practical knowledge acquired in his field.
Interpreting and forming new types of knowledge by combining the knowledge from the area and the knowledge from other field.
Solves the issues of his field by using scientific methods.
The ability to carry out a specialist study related to his area independently
Uses computer programs competent enough for his department.
Using communication technologies efficiently and according to the needs of the department.
Collects data related to the field, reviews and makes conclusions; implements and shares them by considering ethical values.
Internalizes the knowledge gained in the field, transforms it into a skill and uses it with interdisciplinary studies.

-- MODE OF DELIVERY
The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE 1. Week Probability, conditional probability and independence 2. Week The binomial, the multinomial and the Poisson distribution 3. Week Odds ratio 4. Week Log-linear models for two dimensional tables 5. Week Simple logistic regression 6. Week Three-Dimensional tables,Simpson paradox,odds ratio,log linear models 7. Week log-linear models for three dimensions table and testing models and Akaike'Information Criterion 8. Week Mid-term exam 9. Week Multiple Logistic regression, 10. Week Graphical models and independence relationships 11. Week Model selection methods and model evaluation 12. Week Markov models 13. Week Psuedo-Bayes estimates 14. Week Sampling methods for discrete models and Asymptotic models 15. Week Final exam 16. Week -----------------------------
-- TEACHING and LEARNING METHODS
-- ASSESSMENT CRITERIA
 Quantity Total Weighting (%) Midterm Exams 1 20 Assignment 2 20 Application 0 0 Projects 1 30 Practice 0 0 Quiz 0 0 Percent of In-term Studies 70 Percentage of Final Exam to Total Score 30
-- WORKLOAD
 Activity Total Number of Weeks Duration (weekly hour) Total Period Work Load Weekly Theoretical Course Hours 14 3 42 Weekly Tutorial Hours 0 Reading Tasks 10 2 20 Searching in Internet and Library 11 2 22 Material Design and Implementation 9 2 18 Report Preparing 7 3 21 Preparing a Presentation 6 3 18 Presentation 1 3 3 Midterm Exam and Preperation for Midterm Exam 5 3 15 Final Exam and Preperation for Final Exam 9 3 27 Other (should be emphasized) 0 TOTAL WORKLOAD: 186 TOTAL WORKLOAD / 25: 7.44 Course Credit (ECTS): 7.5
-- COURSE'S CONTRIBUTION TO PROGRAM
 NO PROGRAM LEARNING OUTCOMES 1 2 3 4 5 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 X 9 X 10 X
-- NAME OF LECTURER(S)
(Prof. Dr. Bülent ÇELİK)
-- WEB SITE(S) OF LECTURER(S)
(https://abs.gazi.edu.tr/bucelik)
-- EMAIL(S) OF LECTURER(S)
(bucelik@gazi.edu.tr)