Hiroshima University Syllabus

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Japanese
Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA112001 Subject Classification Specialized Education
Subject Name 推測統計学
Subject Name
(Katakana)
スイソクトウケイガク
Subject Name in
English
Inferential Statistics
Instructor YANAGIHARA HIROKAZU
Instructor
(Katakana)
ヤナギハラ ヒロカズ
Campus Higashi-Hiroshima Semester/Term 2nd-Year,  First Semester,  1Term
Days, Periods, and Classrooms (1T) Tues5-8:ENG 103
Lesson Style Lecture Lesson Style
(More Details)
 
Lecture-oriented, Note-taking, Teams, Bb9 
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) 01 : Mathematics/Statistics
Eligible Students
Keywords Random variable, probability distribution, point estimation, interval estimation 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Program
 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Knowledge and Understanding)
・D1. Knowledge and skills required for understanding the theoretical system of statistics and data analysis, and for precisely and efficiently analyzing qualitative/quantitative information in big data.

(Abilities and Skills)
・A. Skills related to the development of an information infrastructure,information processing techniques, and technology for producing new added value through data analysis.

・ B. Ability to identify and solve new problems on their own by quantitative and logical thinking based on data, diverse perspectives, and advanced skills for information processing and analysis.
 
Class Objectives
/Class Outline
We study elementary inference statistics 
Class Schedule lesson1 Population and statistical model
lesson2 Random variable
lesson3 Expectation
lesson4 Various probability distributions
lesson5 Convergences of random variable
lesson6 Point estimation
lesson7 Unbiasedness, variance, mean square error
lesson8 Consistency
lesson9 Asymptotic normality
lesson10 Least square estimation
lesson11 Least square estimation for simple regression
lesson12 Maximum likelihood estimation
lesson13 Maximum likelihood estimation under normality
lesson14 Interval estimation
lesson15 Interval estimations in various settings

There might be some small changes on lessons 
Text/Reference
Books,etc.
Not specified 
PC or AV used in
Class,etc.
 
(More Details) Hand-out, PC 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Review
Please do not hesitate to ask a question if you have dubious points 
Requirements You can choose between face-to-face, online, and on-demand lecture formats. You can choose between face-to-face, online, and on-demand lecture formats, but the number of face-to-face participants will be limited to avoid crowding. 
Grading Method Tasks and Report 
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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