Hiroshima University Syllabus

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Academic Year 2024Year School/Graduate School School of Informatics and Data Science
Lecture Code KA123001 Subject Classification Specialized Education
Subject Name カテゴリカル・データ分析(CDA)
Subject Name
Subject Name in
Basic and practice in Categorical data analysis
Instructor MONDEN REI
モンデン レイ
Campus Higashi-Hiroshima Semester/Term 2nd-Year,  Second Semester,  4Term
Days, Periods, and Classrooms (4T) Thur1-4:EDU K201
Lesson Style Lecture Lesson Style
(More Details)
Credits 2.0 Class Hours/Week   Language of Instruction B : Japanese/English
Course Level 2 : Undergraduate Low-Intermediate
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 02 : Information Science
Eligible Students Second year Bachelor 4th semester
Keywords Categorical Data Analysis, Generalized Linear Model, R program 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
(Applicable only to targeted subjects for undergraduate students)
Learn statistical knowledge and be able to apply them 
Criterion referenced
(Applicable only to targeted subjects for undergraduate students)
Computer Science Program
(Abilities and Skills)
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Data Science Program
(Comprehensive Abilities)
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.

Intelligence Science Program
(Comprehensive Abilities)
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science. 
Class Objectives
/Class Outline
To understand categorical data analysis methods and its application in R program 
Class Schedule lesson1: Guidance, Rmarkdown
lesson2: Review
lesson3: Analyzing contingency tables (theory)
lesson4: Analyzing contingency tables (practical)
lesson5: Generalized Linear Models (theory)
lesson6: Generalized Linear Models (practical)
lesson7: Logistic regression (theory)
lesson8: Logistic regression (practical)
lesson9: Multiple logistic regression
lesson10: Building and applying logistic regression models (theory)
lesson11: Building and applying logistic regression models (practical)
lesson12: Multicategory logit models
lesson13: Models for matched pairs
lesson14: Generalized Linear Mixed models
lesson15: Loglinear models for contingency tables and counts

An assignment will be given for each lecture. By handing in the assignments, your attendance will be counted.   
Reference: (I will explain about this on the 1st lecture about this. No need to buy)
An Introduction to Categorical Data Analysis 3rd ed. Alan Agresti 
PC or AV used in
(More Details) PC(each student is expected to bring their own PC to program during the course) 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Reading the reference can be helpful to prepare and review the lectures 
Requirements The lecture follows a hybrid style (i.e., face-to-face and online). 
Grading Method Final report (55%), Assignments (40%), Attitude toward the class (5%).
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. 
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