| Academic Year |
2025Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA241301 |
Subject Classification |
Specialized Education |
| Subject Name |
数理統計学 |
Subject Name
（Katakana） |
スウリトウケイガク |
Subject Name in
English |
Mathematical Statistics |
| Instructor |
WAKAKI HIROFUMI |
Instructor
(Katakana) |
ワカキ ヒロフミ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Tues1-2,Thur1-2 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| Lecture |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
J
:
Japanese |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
B3 |
| Keywords |
low of large numbers, central limit theorem, population and sample, estimation |
| 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) | 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.
・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.
Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・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. |
|---|
Class Objectives
/Class Outline |
Study theorems on convergence of ramdom variable, derivation of the distribution of statistics, and estimations |
| Class Schedule |
lesson1:Review contents of Probability space, random variable and its distribution
lesson2-4: Convergence of random variables
convergence in probability and in distribution, low of large numbers, central limit theorem, almost sure convergence, asymptotic theories
lesson5-8: Population distribution and statistical models
sample, population distribution and statistical models, distributions of statistics, order statistics
lesson9-12: Estimation
point estimation, least square method, unbiased estimator, likelihood method, interval estimation
lesson12-15: Testing hypothesis
lesson 15: Exercise |
Text/Reference
Books,etc. |
「確率・統計の数学的基礎」（藤越、若木、栁原著） |
PC or AV used in
Class,etc. |
Text, Handouts, Microsoft Teams |
| (More Details) |
|
| Learning techniques to be incorporated |
Discussions, Quizzes/ Quiz format |
Suggestions on
Preparation and
Review |
Read the text book before each lecture and solve the problems in it. |
| Requirements |
|
| Grading Method |
result of tests |
| 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. |