| Course title | |||||
| 数理統計学 [Mathematical Statistics] | |||||
| Course category | technology speciality courses | Requirement | Credit | 2 | |
| Department | Year | 2～4 | Semester | 1st | |
| Course type | 1st | Course code | 022251 | ||
| 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 11:09:12 AM |