2026 (Current Year) Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Quality Management
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Ryuji Uozumi
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (W9-326) / 5-6 Fri (W9-326)
- Class
- -
- Course Code
- IEE.C302
- Number of credits
- 110
- Course offered
- 2026
- Offered quarter
- 4Q
- Syllabus updated
- Sep 7, 2026
- Language
- Japanese
Syllabus
Course overview and goals
This course covers statistical methods used in quality management, such as statistical quality control, multiple regression, analysis of variance, design of experiments, 7 QC tools, and reliability management. Students perform data analysis using R for learning multiple regression and analysis of variance. Students are required to work on group works for learning quality management, design of experiments, and analysis of variance.
Course description and aims
Students will be able to:
(1) Understand statistical methods used in quality management and the basic concepts of quality management.
(2) Perform data analysis using linear models and interpret the results.
(3) Design the efficient experiments, collect data, and perform data analysis.
Keywords
Statistical Quality Control, Analysis of Variance, Multiple Regression, Multivariate Analysis, Design of Experiments, Reliability Engineering
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Give a lecture, group discussions, and group works on each topic.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Orientation, review of statistical topics |
Understand course overview |
| Class 2 | Review of statistical inference |
Understand statistical inference |
| Class 3 | Estimation of linear models |
Understand the estimation of linear models using matrix |
| Class 4 | One-way analysis of variance (ANOVA) |
Understand one-way analysis of variance (ANOVA) |
| Class 5 | Assessment of regression models |
Understand multiple regression |
| Class 6 | Discuss quality control |
Group discussions |
| Class 7 | 7 QC tools |
Understand 7 QC tools |
| Class 8 | Design of experiments, sample size calculation |
Understand design of experiments |
| Class 9 | Group work (1) |
Pilot study |
| Class 10 | Group work (2) |
Data collection and data analysis |
| Class 11 | Two-way ANOVA |
Understand two-way ANOVA |
| Class 12 | Group work (3) |
Data collection and data analysis |
| Class 13 | Group work (4) |
Presentation |
| Class 14 | Final exam |
Check the level of understanding |
Study advice (preparation and review)
To enrich 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 materials.
Textbook(s)
Provide handouts on each topic.
Reference books, course materials, etc.
N/A
Evaluation methods and criteria
Final exam, report, and group work output.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
Prerequisites
Prior completion of "Probability for Industrial Engineering and Economics" and "Statistics for Industrial Engineering and Economics" or equivalent is required.
Other
Bring your laptop with statistical software (R and R Studio).