KA123001 Basic and practice in Categorical data analysis

Hiroshima University Syllabus

Japanese

Academic Year 2025Year 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
（Katakana）カテゴリカル・データブンセキ（シーディーエー）

Subject Name in
English Basic and practice in Categorical data analysis

Instructor MONDEN REI

Instructor
(Katakana) モンデン　レイ

Campus Higashi-Hiroshima Semester/Term 2nd-Year,  Second Semester,  4Term

Days, Periods, and Classrooms (4T) Thur1-4：IAS K108

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive), Online (on-demand)

Credits 2.0 Class Hours/Week 4 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
Program
(Applicable only to targeted subjects for undergraduate students) Learn statistical knowledge and be able to apply them
Criterion referenced
Evaluation
(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: Final Exam

An assignment will be given for each lecture. By handing in the assignments, your attendance will be counted.
In addition, a final exam (paper exam) will be given.

Text/Reference
Books,etc. An assignment will be assigned for each lecture, and submitting them will count toward your attendance. Additionally, there will be a final paper-based exam.

PC or AV used in
Class,etc. Text, Handouts, Microsoft Teams, Other (see [More Details])

(More Details) PC (each student is expected to bring their own PC to program during the course)

Learning techniques to be incorporated Post-class Report

Suggestions on
Preparation and
Review Reading the reference can be helpful to prepare and review the lectures

Requirements Please make sure to attend the first lecture and the final lecture (exam) in the classroom.

Grading Method Final exam (55%), Assignments (40%), Attitude toward the class (5%).

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message For English-speaking students, the lectures will be available only in an on-demand format.
The final exam will be conducted in English (multiple-choice) in the classroom.

Other

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.

Academic Year	2025Year	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 （Katakana）	カテゴリカル・データブンセキ（シーディーエー）
Subject Name in English	Basic and practice in Categorical data analysis
Instructor	MONDEN REI
Instructor (Katakana)	モンデン　レイ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Thur1-4：IAS K108
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive), Online (on-demand)

Credits	2.0	Class Hours/Week	4	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 Program (Applicable only to targeted subjects for undergraduate students)	Learn statistical knowledge and be able to apply them
Criterion referenced Evaluation (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: Final Exam An assignment will be given for each lecture. By handing in the assignments, your attendance will be counted. In addition, a final exam (paper exam) will be given.
Text/Reference Books,etc.	An assignment will be assigned for each lecture, and submitting them will count toward your attendance. Additionally, there will be a final paper-based exam.
PC or AV used in Class,etc.	Text, Handouts, Microsoft Teams, Other (see [More Details])
(More Details)	PC (each student is expected to bring their own PC to program during the course)
Learning techniques to be incorporated	Post-class Report
Suggestions on Preparation and Review	Reading the reference can be helpful to prepare and review the lectures
Requirements	Please make sure to attend the first lecture and the final lecture (exam) in the classroom.
Grading Method	Final exam (55%), Assignments (40%), Attitude toward the class (5%).
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	For English-speaking students, the lectures will be available only in an on-demand format. The final exam will be conducted in English (multiple-choice) in the classroom.
Other
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.

Back to syllabus main page