| Course title | |||||
| 数理統計学 [Mathematical Statistics] | |||||
| Course category | Requirement | Credit | 2 | ||
| Department | Year | 2～ | Semester | 3rd | |
| Course type | 3rd | Course code | 01ma2005a | ||
| Instructor(s) | |||||
| 與口 卓志 [YOGUCHI Takashi] | |||||
| Facility affiliation | Graduate School of Agriculture | Office | Email address | ||
| Course description |
| Mathematical statistics is a tool of estimating the property of the population (a large set of data) from its small sample. In this course, we will first introduce basic concepts and terms of statistics such as means, variances, probability distributions, etc. Then, observing the connection between statistical properties of samples and those of the population, we will learn methods of interval estimation and hypothesis testing. |
| Expected Learning |
|
The goal of this course is to be capable of (1) understanding standard terms and notations of mathematical statistics, (2) calculating the mean and standard deviation of a given set of numbers, (3) applying normal distribution or Student's t-distribution to estimation of the mean of the population, (4) testing a statistical hypothesis by using normal distribution. Corresponding criteria in the Diploma Policy: See the Curriculum maps. (URL: https://www.tuat.ac.jp/campuslife_career/campuslife/policy/ ) |
| Course schedule |
|
Google Classroom will be used for quizzes and assignments. [Google Classroom Code] idyiccj 1. Means and variances, standard deviations (pp.6-7, pp.10-14, pp.18-21) 2. Random variables, distribution functions (pp.34-40) 3. Joint random variables (pp.42-43, p.47) 4. Covariances and correlation coefficients (pp.22-25, pp.48-49) 5. Means and variances of the sum of random variables, independence (pp.44-52) 6. Binomial distribution and normal distribution (pp.54-58) 7. Applications of normal distribution, Poisson distribution (pp.59-63) 8. Sampling distributions, the central limit theorem (pp.74-81) 9. Interval estimations I, unbiased variance (pp.82-87) 10. Student's t-distribution, interval estimations II (p.67, pp.84-90) 11-12. Hypothesis tests (pp.92-102, pp.109-110) 13. Equal mean hypothesis (pp.102-105) 14. Chi-square tests and independence tests (pp.112-119) 15. 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 |
| Heiji Kodera, “Shin Toukei Nyuumon”, Shoukabou (in Japanese) |
| References |
| P. G. Hoel, “Elementary Statistics”, John Wiley & Sons, Inc., New York. |
| Assessment/Grading |
|
The term examination, 70%; Quizzes in Google Classroom, 10%; Assignments, 20% |
| Message from instructor(s) |
| It seems that leaners are likely to be embarrassed by some confusing terms (such as “mean of sample mean”). So, when a new word appears, please be aware of considering the meaning of it. |
| Course keywords |
| Random variable, Mean, Variance, Probability distribution, Central limit theorem, Interval estimation, Hypothesis test |
| Office hours |
| It will be announced in the first lecture. |
| Remarks 1 |
| The schedule may be amended due to COVID-19. In this case, the change will be announced in Google Classroom. |
| Remarks 2 |
| Related URL |
| Lecture Language |
| Japanese |
| Language Subject |
| Last update |
| 9/20/2022 1:15:23 PM |