| Course title | |||||
| 数理統計学 [Mathematical Statistics] | |||||
| Course category | technology speciality courses,ets. | Requirement | Credit | 2 | |
| Department | Year | 2～4 | Semester | Fall | |
| Course type | Fall | Course code | 022111 | ||
| Instructor(s) | |||||
| 宮田 敏 [MIYATA Satoshi] | |||||
| Facility affiliation | Graduate School of Engineering | Office | Email address | ||
| Course description |
| This course emphasizes statistical thinking rather than mathematical details and is intended to get students familiar with organizing and describing data, as well as with basic statistical reasoning and models for data analysis including regression and ANOVA (Analysis of Variance). Students will be supposed to use a computer intensively but no previous knowledge of a computer is required. |
| Expected Learning |
|
・ To understand the objectives of statistical data analysis, and the basic concepts such as confidence interval and/or hypothesis testing. ・ To analyze data using basic data analysis technique by R, and interpret the results. ・ To be able to apply statistical models, including regression analysis and ANOVA (analysis of variance) to practical data. |
| Course schedule |
|
1. Introduction. Basic paradigm of statistics 2. Descriptive statistics. Numerical and graphical summary of data 3. Probability 4. Random variable and probability distribution 5. Normal distribution and jointly distributed random variables 6. Point estimation, Confidence interval, Hypothesis testing 7. One sample problem 8. Two sample problem (Midterm examination, take home) 9. Analysis of Variance (ANOVA) 10. Two-Factor Analysis of Variance, Multiple comparisons 11. ANOVA, Model diagnostics and transformation of variable 12. (Simple) linear regression 13. Multiple regressions 14. Regression diagnostics and transformation of variables 15. Non-linear regression, Logistic regression, etc. (Final examination, take home) |
| Prerequisites |
| None. |
| Required Text(s) and Materials |
| None. Lecture notes will be distributed on moodle. |
| References |
|
・ Peter Dalgaard, “Introductory Statistics With R, 2nded.”, Springer-Verlag, ISBN: 978-0387790534 ・ W. N. Venables & Brian D. Ripley, “Modern Applied Statistics With S, 4th Revised edition”, Springer-Verlag, ISBN: 978-1441930088 ・ 東京大学教養学部統計学教室 (編集)「統計学入門」東京大学出版会 (1991/7/9) ISBN-13: 978-4130420655 ・ 石村 貞夫，石村 光資郎「入門はじめての分散分析と多重比較」東京図書 (2008/01) ISBN-13: 978-4489020292 ・ 舟尾 暢男「The R Tips―データ解析環境Rの基本技・グラフィックス活用集」オーム社; 第3版 (2016/10/13) ISBN-13: 978-4274219580（少々高価なので、代わりに以下のURLを参照しても可。 R-Tips http://cse.naro.affrc.go.jp/takezawa/r-tips/r.html） |
| Assessment/Grading |
|
・ Home work 50% ・ Midterm examination 20% ・ Final examination 30% |
| Message from instructor(s) |
| Feel free to interrupt me during the lecture, or send me an email to ask your question. |
| Course keywords |
| Mathematical statistics, data analysis, confidence interval, hypothesis testing, regression analysis, ANOVA (analysis of variance) |
| Office hours |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
| Language Subject |
| Last update |
| 10/6/2017 9:02:43 AM |