2024 Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Statistics for Industrial Engineering and Economics
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Ryutaro Ichise
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Tue / 1-2 Fri
- Class
- -
- Course Code
- IEE.A205
- Number of credits
- 110
- Course offered
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- Mar 17, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In this lecture, we will discuss statistical methods and machine learning methods, which form the core of data science, as engineering approaches to solving various industrial engineering and economics problems. In the field of statistics, we will discuss mean and variance, statistical estimation, statistical hypothesis testing, and regression analysis. In the field of machine learning, we will discuss classification and clustering.
In this lecture, students will acquire basic knowledge of statistical views and ideas that form the basis of data science, frameworks for statistical decision-making, and basic machine learning methods. In addition, students will be able to apply them to industrial engineering and economics problem-solving.
Course description and aims
By taking this course, students will be able to acquire the following skills.
(1) Basic knowledge of statistical estimation, statistical testing, and machine learning for data handling methods.
(2) To be able to calculate, interpret, and explain basic statistics using numbers and figures.
(3) To be able to use data to solve engineering problems using statistical and machine learning methods.
Keywords
Point estimation, interval estimation, hypothesis testing, regression analysis, classification, clustering
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Give a lecture and give some exercise problems. Solutions for the exercise problems are also reviewed.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Basics of Statistics | Understand basics of statistics |
| Class 2 | Probability | Understand random variables and probability distributions |
| Class 3 | Statistical distribution & Sampling (1) | Understand binomial distribution and normal distribution |
| Class 4 | Statistical distribution & Sampling (2) | Understand Chi-square distribution and Student’s t distribution |
| Class 5 | Estimation | Understand point estimation and interval estimation |
| Class 6 | Hypothesis testing (1) | Understand basics of hypothesis testing methods |
| Class 7 | Hypothesis testing (2) | Understand hypothesis testing methods for various settings |
| Class 8 | Bivariate data | Understand statistical methods for handling bivariate data |
| Class 9 | Regression analysis | Understand regression analysis |
| Class 10 | Machine learning | Understand basics of machine learning |
| Class 11 | Predictive performance evaluation | Understand how to evaluate the performance of machine learning methods |
| Class 12 | Clustering | Understand data clustering methods |
| Class 13 | Dimension reduction | Understand methods of dimension reduction for large scale data |
| Class 14 | Conclusion | Understand how to apply statistical methods to engineering problems |
Study advice (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 afterward (including assignments) for each class.
Textbook(s)
Nobuaki Obata: Probability and Statistics for Data Science, Kyoritsu Shuppan (in Japanese)
Reference books, course materials, etc.
Akira Suzuki: Algorithm for Machine Leaning, Kyoritsu Shuppan (in Japanese)
Kazunori Matsumoto, Tetsuhiro Miyahara, Yasuo Nagai, Ryutaro Ichise: Artificial Intelligence, Ohm Sha (in Japanese)
Provide handouts when needed.
Evaluation methods and criteria
Exercise problems and Final exam.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A331 ： OR and Modeling
- IEE.C302 ： Quality Management
Prerequisites
Students must have successfully completed "Probability for Industrial Engineering and Economics" or have equivalent knowledge.