▶Japanese
Post to SNS
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Yamamuro Kyoko Kurisu Daisuke
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon7-8(W932) Thr7-8(W932)
- Group
- -
- Course number
- IEE.B334
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 2Q
- Syllabus updated
- 2021/3/19
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
In this course, we study basics of probability theory and mathematical statistics. In addition, we study linear regression models, their nonlinear extension, and nonparametric methods to be able to use appropriate statistical methods for data analysis.
Student learning outcomes
This course aims to grasp the idea of well-known statistical methods and master those methods.
Keywords
independence of random variables, conditional probability, Bayes' formula, univariate and multivariate probability distributions, moment generating function, characteristic function, low of large numbers, central limit theorem, Slutsky' theorem, delta method, maximum likelihood estimator, confidence interval, linear regression, panel analysis, nonparametric method.
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
We first study basic probability distributions which is often used in econometrics and statistics (classes 1-4). Then we study basic probability theory and asymptotic theory (classes 5-10). We introduce linear regression models and study asymptotic properties of least square estimators (classes 11-12). As extension of linear models, we also introduce some nonlinear regression other linear models models such as panel analysis and study those statistical properties (class 13). In class 14, we focus on nonparametric kernel method which are also often used in econometrics and statistics.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Overview of this course. | Understand the purpose of this course. |
| Class 2 | Random variables and those properties. | Understand the definition of random variables and those properties. |
| Class 3 | Univariate probability distributions. | Understand univariate probability distributions. |
| Class 4 | Multivariate probability distributions. | Understand multivariate probability distributions. |
| Class 5 | Moment generating function and characteristic function. | Understand moment generating function and characteristic function. |
| Class 6 | Law of large numbers. | Understand law of large numbers. |
| Class 7 | Applications of law of large numbers. | Understand applications of law of large numbers. |
| Class 8 | Central limit theorems. | Understand central limit theorems. |
| Class 9 | Applications of central limit theorems. | Understand applications of central limit theorems. |
| Class 10 | Maximum likelihood estimator and related topics. | Understand maximum likelihood estimator and related topics. |
| Class 11 | Consistency of least square estimators. | Understand asymptotic normality of least square estimators. |
| Class 12 | Asymptotic normality of least square estimators. | Understand simple econometric models. |
| Class 13 | Asymptotic properties of estimators for panel data analysis. | Understand panel data analysis. |
| Class 14 | Definition and asymptotic properties of kernel density/ regression estimators. | Understand asymptotic properties of kernel density/regression estimators. |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None.
Reference books, course materials, etc.
A reading list covering fundamental studies for each topic will be available in class.
Assessment criteria and methods
The final grade is determined based on (i) class attendance (35%) and (ii) final report (65%).
Precisely, students are required to submit pdf files of (i) mid term assignments to confirm the degree of comprehension (i) and (ii) solutions of some exercises as a final report via the OCW-i system.
Related courses
- IEE.B207 ： Econometrics I
- IEE.B301 ： Econometrics II
- IEE.B405 ： Advanced Econometrics
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A204 ： Probability for Industrial Engineering and Economics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
To take this course, students should be proficient in pre-intermediate linear algebra, mathematical analysis, econometrics, and mathematical statistics.
