Course title
数理統計学   [Mathematical Statistics]
Course category technology speciality courses  Requirement   Credit 2 
Department   Year 34  Semester 1st 
Course type 1st  Course code 023551
Instructor(s)
鈴木 良一   [SUZUKI Riyouichi]
Facility affiliation Graduate School of Engineering Office   Email address

Course description
Mathematical statistics is the study of giving some methods for estimating the nature of general populations rationally from experimental data and measurements. In this course, basic concepts (such as, probability distribution, mean, variance, standard deviation, and random sample) and representative methods of statistical inferences (such as, point estimation, interval estimation, and hypothesis testing) will be learned.

This course is taught by a part-time lecturer. Once the employment of the part-time lecturer is confirmed, this syllabus may be modified. In this case, the official version is the modified syllabus.
Expected Learning
The goals of this course are
(1) to understand both the basic concepts of mathematical statistics and the methods of point estimation, interval estimation, and hypothesis testing, and
(2) to be good at simple practical calculations.
Corresponding criteria in the Diploma Policy: See the Curriculum maps
Course schedule
1. Basic methods for data collation
2. Random variable and probability distribution I
3. Random variable and probability distribution II
4. Most simple populations and their samples
5. Estimation I
6. Hypothesis testing I
7. Confidence interval
8.Review, and midterm examination
9. Normal distribution
10. Estimation II
11. Hypothesis testing II
12. Hypothesis testing III
13. Tests relating to contingency tables
14. Linear models and variance analysis
15.Review, and Term examination
Prerequisites
In addition to 30 hours that students spend in the class, students are recommended to prepare for and revise the lectures, spending the standard amount of time as specified by the University and using the lecture handouts as well as the references specified below.
Required Text(s) and Materials
稲垣宣生・吉田光雄・山根芳知・地道正行「データ科学の数理 統計学講義」裳華房, 2007年
References
尾畑伸明「数理統計学の基礎」共立出版, 2014年
Assessment/Grading
It will be announced in Google Classroom.
Message from instructor(s)
Course keywords
Mean, Variance, Normal distribution, Sample, Estimation, Testing
Office hours
It will be announced in the first lecture.
Remarks 1
Remarks 2
Related URL
Lecture Language
Japanese
Language Subject
Last update
1/19/2022 11:12:23 AM