| Academic Year |
2024Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA217001 |
Subject Classification |
Specialized Education |
| Subject Name |
ノンパラメトリック解析 |
Subject Name
（Katakana） |
ノンパラメトリックカイセキ |
Subject Name in
English |
Nonparametric analysis |
| Instructor |
WAKAKI HIROFUMI |
Instructor
(Katakana) |
ワカキ ヒロフミ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, First Semester, 2Term |
| Days, Periods, and Classrooms |
(2T) Weds7-8,Fri5-6：ENG 218 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
Lecture and exercise.
|
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
|
| Keywords |
Wilcoxon's signed rank test, Mann-Whitney U test, Ansari-Bradley test, Kolmogorov-Smirnov test, Kruskal-Wallis test. |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | A subject to be leaned after studying basic statistical inferences |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. |
|---|
Class Objectives
/Class Outline |
Learn about the theory of nonparametric tests, and exercises with using R. |
| Class Schedule |
lesson1:Influence on the t-test by non normality (lecture)
lesson2:Using R (Exercize)
lesson3:Influence on the t-test by non normality (exercise)
lesson4:Wilkoxon's signed rank test (lecture)
lesson5:Wilkoxon's signed rank test (exercise)
lesson6:Wilkoxon's rank sum test - Mann-Whitney U test (lecture)
lesson7:Wilkoxon's rank sum test - Mann-Whitney U test (exercise)
lesson8:Influence on the Bartlett's test by non normality (lecture)
lesson9:Influence on the Bartlett's test by non normality (exercise)
lesson10:Ansari-Bradley test (lecture)
lesson11:Ansari-Bradley test (exercise)
lesson12:Kolmogorov-Smirnov test (lecture)
lesson13:Kolmogorov-Smirnov test (exercise)
lesson14:ANOVA and Kruskal-Walis test (lecture)
sson15:ANOVA and Kruskal-Walis test (exercise)
reports |
Text/Reference
Books,etc. |
Handout |
PC or AV used in
Class,etc. |
|
| (More Details) |
PC (Laptop as a Requisite Tool) |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Ask questions soon in the class or after the class if you cannot understand |
| Requirements |
|
| Grading Method |
reports |
| 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. |