| Course title | |||||
| 数理統計学 [Mathematical Statistics] | |||||
| Course category | Requirement | Credit | 2 | ||
| Department | Year | 2～ | Semester | 3rd | |
| Course type | 3rd | Course code | 01ma2005c | ||
| Instructor(s) | |||||
| 川森 愛, 加用 千裕 [KAWAMORI Ai, KAYO Chihiro] | |||||
| Facility affiliation | Graduate School of Agriculture | 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. |
| Expected Learning |
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The goal of this course is (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 statistical tests. Corresponding criteria in the Diploma Policy: See the Curriculum maps. URL: https://www.tuat.ac.jp/campuslife_career/campuslife/policy/ |
| Course schedule |
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1．Orientation: Outline what is statistics and the goal of this course 2．The basics of statistics: mean, variance, correlation, how to draw and read graphs, types of data 3．Probability distribution 4．Population and samples 5．Estimation 1：point estimation, interval estimation 6．Estimation 2：exercise 7．Hypothesis testing 1：test procedures, hypotheses, test statistics, significance, and the test for the difference between means 8．Hypothesis testing 2：various tests, non-parametric tests 9．Hypothesis testing 3：meaning and properties of p-value 10．Hypothesis testing 4：exercise 11．Prediction 1：regression analysis 12．Prediction 2：least square, likelihood, maximum likelihood estimation(MLE) 13．Prediction 3：fitness of regression model 14．Prediction 4：exercise 15．Beyond the basics: generalized linear model(GLM), Bayesian statistics |
| Prerequisites |
| In addition to 30 hours in classes and time required for completing assignments, students are recommended to prepare for and review the lectures, spending the standard amount of time as specified by the University and using the lecture handouts as well as the references. |
| Required Text(s) and Materials |
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| References |
| Assessment/Grading |
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1．Attendance(55%) 2．Report(45%) |
| Message from instructor(s) |
| Questions are always accepted before and after the class and by e-mail |
| Course keywords |
| mean, variance, probability distribution, population, parameter, samples, estimation, hypothesis testing, prediction, regression analysis |
| Office hours |
| Remarks 1 |
| In this lecture, we will practice using Excel |
| Remarks 2 |
| Related URL |
| Lecture Language |
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| Last update |
| 9/29/2022 5:04:22 PM |