WSA56000 Topics in Probability and Mathematical Statistics B

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Mathematics Program

Lecture Code WSA56000 Subject Classification Specialized Education

Subject Name 確率統計特論Ｂ

Subject Name
（Katakana）カクリツトウケイトクロンビー

Subject Name in
English Topics in Probability and Mathematical Statistics B

Instructor WAKAKI HIROFUMI

Instructor
(Katakana) ワカキ　ヒロフミ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Mon3-4,Weds3-4：ECON B157

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

Lecture

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 6 : Graduate Advanced

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students

Keywords contingency table, logistic regression, poisson regression, category data

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline Basic methods of analysing categorical data are explained, which include 2x2 tables, logistic regression, poisson regression and multinomial logit model.

Class Schedule lesson1:Statistical model, maximal likelihood estimator and its asymptotic property
lesson2:Testing hypotheses, test using likelihood and its asymptotic property
lesson3:Inference about multinomial distribution
lesson4:Inference about poisson distribution
lesson5:Goodness of fit test
lesson6:Analysis of binary data (two-by-two table)
lesson7:Analysis of binary data (comparison of multi-samples)
lesson8:Analysis of binary data (comparison of sebral two-by-two tables)
lesson9:Logistic regression
lesson10:Over dispersion
lesson11:Poisson regression
lesson12:Multinomial logistic regression
lesson13:Independency
lesson14:Ordered category data
lesson15:Inference based on the conditional distribution

reports

Text/Reference
Books,etc. Distribute handouts as necessary

PC or AV used in
Class,etc. moodle

(More Details) Slides are used sometimes.

Statistical Analysis of Categorical Data, by Chris J. Lloid, Wiley Series of Probability and Statistics.

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.

Academic Year	2026Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Mathematics Program
Lecture Code	WSA56000	Subject Classification	Specialized Education
Subject Name	確率統計特論Ｂ
Subject Name （Katakana）	カクリツトウケイトクロンビー
Subject Name in English	Topics in Probability and Mathematical Statistics B
Instructor	WAKAKI HIROFUMI
Instructor (Katakana)	ワカキ　ヒロフミ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Mon3-4,Weds3-4：ECON B157
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (on-demand)
Lecture
Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	6 : Graduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students
Keywords	contingency table, logistic regression, poisson regression, category data
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	Basic methods of analysing categorical data are explained, which include 2x2 tables, logistic regression, poisson regression and multinomial logit model.
Class Schedule	lesson1:Statistical model, maximal likelihood estimator and its asymptotic property lesson2:Testing hypotheses, test using likelihood and its asymptotic property lesson3:Inference about multinomial distribution lesson4:Inference about poisson distribution lesson5:Goodness of fit test lesson6:Analysis of binary data (two-by-two table) lesson7:Analysis of binary data (comparison of multi-samples) lesson8:Analysis of binary data (comparison of sebral two-by-two tables) lesson9:Logistic regression lesson10:Over dispersion lesson11:Poisson regression lesson12:Multinomial logistic regression lesson13:Independency lesson14:Ordered category data lesson15:Inference based on the conditional distribution reports
Text/Reference Books,etc.	Distribute handouts as necessary
PC or AV used in Class,etc.	moodle
(More Details)	Slides are used sometimes. Statistical Analysis of Categorical Data, by Chris J. Lloid, Wiley Series of Probability and Statistics.
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.

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