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
1X
2X
3X
4X
5X
6X
7X
8X
9X
10X
 -- 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)