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- 工学院，物質理工学院，環境・社会理工学院共通科目
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School of Environment and Society
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- 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)
- Uozumi Ryuji
- Class Format
- Lecture / Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- IEE.C302
- Credits
- 2
- Academic year
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- 2024/3/14
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course covers statistical methods used in quality management, such as statistical quality control, multiple regression, analysis of variance, nonparametric method, design of experiments, 7 QC tools, and reliability management. Students perform data analysis using R for learning multiple regression. Students are required to work on group works for learning quality management, design of experiments, and analysis of variance.
Student learning outcomes
Students will be able to:
(1) Understand statistical methods used in quality management and the basic concepts of quality management. (2) Derive the hypotheses.
(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, Nonparametric Method, Multivariate Analysis, Design of Experiments, Reliability Engineering
Competencies that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Orientation, review of statistical topics | Understand course overview |
| Class 2 | Review of statistical topics, analysis of variance (ANOVA) | Understand ANOVA and its background |
| Class 3 | One-way ANOVA | Understand one-way ANOVA |
| Class 4 | Estimation of linear models, assessment of regression models | Understand multiple regression |
| Class 5 | Assessment of regression models, Discuss quality control | Understand multiple regression |
| Class 6 | Discuss quality control | Group discussions |
| Class 7 | 7 QC tools, nonparametric methods | Understand 7 QC tools and nonparametric methods |
| 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 |
Out-of-Class Study Time (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
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
Prior completion of "Probability for Industrial Engineering and Economics" and "Statistics for Industrial Engineering and Economics" or equivalent is required.
