| 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
（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：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
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: 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. |
Text/Reference
Books,etc. |
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
Class,etc. |
|
| (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
Review |
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 |
|
| Message |
|
| 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. |