| Course title | |||||
| 数理統計学 [Mathematical Statistics] | |||||
| Course category | technology speciality courses,ets. | Requirement | Credit | 2 | |
| Department | Year | 2～4 | Semester | 3rd | |
| Course type | 3rd | Course code | 022812 | ||
| Instructor(s) | |||||
| 木村 泰紀 [KIMURA Yasunori] | |||||
| Facility affiliation | Graduate School of Engineering | Office | Email address | ||
| Course description |
| This course provides students fundamental concepts and analyzing techniques of mathematical statistics The topics covered in this course include the definition of probability and probability spaces, random variable and its independency, distribution of random variables such as binomial distribution, normal distribution, etc., and important theorems such as the central limit theorem, the law of large numbers, etc. The cource also deals with the analyzing techniques such as the point estimation, the interval estimation, and the statistical hypothesis testing. |
| Expected Learning |
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Learners who complete this cource will be able to: (1) Explain the basic concepts of mathematical statistics using the words of mathematics; (2) Recognize the various type of mathmatical distributions; (3) Execute the statistical estimation and testing in an appropriate way. |
| Course schedule |
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Week 1: Definition of probability (probability space,and probability) Week 2: Conditional probability and indenendency (independency of probability and the Bayes theorem) Week 3: Random variables (defition and examples) Week 4: Representative values (expectation, variance, and standard deviation) Week 5: Discrete distributions (binomial, geometric, and Poisson distributions) Week 6: Continuous distributions (distribution function and density function) Week 7: Normal distribution (definition and basic properties) Week 8: Population and samples (sampling and its randomness) Week 9: Properties of estimation (unbiasedness, efficiency and consistency) Week 10: Interval estimation (1) (estimation of mean) Week 11: Interval estimation (2) (estimation of ratio) Week 12: Hypothesis testing (1) (definition, testing of mean and variance) Week 13: Hypothesis testing (2) (test of goodness of fit, test of independency) Week 14: Correlation (covariance and correlation) Week 15: Examination |
| Prerequisites |
| Required Text(s) and Materials |
| Documents delivered in the lecture |
| References |
| Announced in the lecture |
| Assessment/Grading |
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Regular exams (90%) and class participation (10%) 2017 grade distribution: S 21%, A 9%, B 30%, C 25%, D 3%, E 12% |
| Message from instructor(s) |
| Course keywords |
| statistics, random vairable, distribution, estimation, testing |
| Office hours |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
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| Last update |
| 3/10/2020 2:07:07 PM |