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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
- 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
- Teacher education courses
- Instructor(s)
- Matsuda Toshiki Hagiuda Nobuko
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Thr5-6(W9-321(W937))
- Group
- -
- Course number
- LAT.A401
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 1Q
- Syllabus updated
- 2023/3/20
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
- Themes of course are statistical methods and multivariable analysis methods as well as the warp and woof model of problem-solving for statistical analysis.
Student learning outcomes
The goal of this course is to master the basic techniques of statistics often used in the areas of education and psychology.
Keywords
Introductory and intermediate statistics
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
The lessons of this course consist of six paired lecture and homework exercise and final review lesson.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Basic Statistics and statistical tests (An E-mail from m-domain address is required until 12:00 on April 14th.) | Exercises by the gaming instructional material |
| Class 2 | Correlation and cross tabulation | Exercises by the gaming instructional material |
| Class 3 | Analysis of variance | Exercises by the gaming instructional material |
| Class 4 | Regression analysis | Exercises by the gaming instructional material |
| Class 5 | Principal component analysis and factor analysis | Exercises by the gaming instructional material |
| Class 6 | Cluster analysis | Exercises by the gaming instructional material |
| Class 7 | Review and Q/A | Review report |
Textbook(s)
Matsuda, T. and Hagiuda, N. (Eds.) (2021) Introduction to data science for problem-solving, Jikkyo Syuppan.
Reference books, course materials, etc.
E-learning materials
(Additional information will be introduced when necessary.)
Assessment criteria and methods
Homework Exercises and final report (Students who did not submit any exercise or final report lose their credits.)
Related courses
- LAT.A403 ： Practices for Psychological and Educational Measurement
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
stat-ask [at] et.hum.titech.ac.jp
Office hours
Office hours by appointment.
