GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
ADVANCE CATEGORICAL DATA ANALYSIS/5601303
Course Title: ADVANCE CATEGORICAL DATA 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
Student learns principle components of categorical data analysis
Student learns frequently used models to model categorical variables and theory underlying these models
Student becomes skillful at applying these methods with the help of statistical software.
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  Basic concepts for categorical data, probability distributions, Maximum likelidood estimation
2. Week  Hypothesis tests, confidence intervals, Goodness-of-Fit tests, sampling and structural zeros
3. Week  2×2, I×2 and I×J contingency tables, sensitivity and specificity, odds ratio, relative risk
4. Week  Chi-square tests for independence
5. Week  Independence tests for ordinal data, exact inference for small samples
6. Week  Three-way contingecy tables, partial tables, Simpson paradox
7. Week  Marginal and conditional odds ratios, conditional independence tests, homogeneous association
8. Week  Log-linear models, parameter estimation and model selection for two-way contingency tables, Mid-term
9. Week  Log-linear models, parameter estimation for three-way contingency tables
10. Week  Log-linear models, parameter estimation and model selection for three-way contingency tables
11. Week  Models for binary responses
12. Week  Models for ordinal contingency tables, row-association (R) model and column-association (C) model
13. Week  Goodman’s RC association model, general association model, conditional association model
14. Week  Project Presentations
15. Week  Final exam
16. Week  -
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
30
 Assignment
0
0
 Application
0
0
 Projects
1
20
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
50
 Percentage of Final Exam to Total Score  
50
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
0
0
 Reading Tasks
9
4
36
 Searching in Internet and Library
7
4
28
 Material Design and Implementation
2
2
4
 Report Preparing
4
4
16
 Preparing a Presentation
4
2
8
 Presentation
4
2
8
 Midterm Exam and Preperation for Midterm Exam
5
4
20
 Final Exam and Preperation for Final Exam
8
3
24
 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
11. Based on the capabilities of undergraduate level, the students enrolled to the program can develop and deepen their knowledge and skill at the level of expertise on the same field of the undergradute study or a different field.X
22. The students use their theoretical and practical knowledge at the level of expertise in the area of statistics.X
33. The students should evaluate their acquired knowledge and skills in a critical perspective and the critical point of view guides their learning process.X
44. Theoretical and practical knowledge gained in graduate level in the field of Statistics should be applied and transfer to the current problems.X
55. By performing the process from the identification of the scientific research problem to reporting and the process should be transferred in oral, written and visual ways.X
66. The students should use computer software and information technologies on the level required by the field of Statistics.X
77. The students should have the ability to use Statistics in interdisciplinary studies.X
88. The students should have enough foreign language level to pursue statistical literature.X
99. At the required level of field of statistics, he/she should use statistical software and information technology efficiently in a such a way that helps solving problems in his/her research.X
1010. In the process of applying knowledge in a professional sense, social, scientific, and ethical values should be regarded.X
 -- NAME OF LECTURER(S)
   (Assoc. Prof. Hülya OLMUŞ)
 -- WEB SITE(S) OF LECTURER(S)
   (https://abs.gazi.edu.tr/hulya)
 -- EMAIL(S) OF LECTURER(S)
   (hulya@gazi.edu.tr)