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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
- Humanities and social science courses
- Instructor(s)
- Kezuka Kazuhiro
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon5-6(W831) Thr5-6(W831)
- Group
- -
- Course number
- LAH.T301
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 4Q
- Syllabus updated
- 2021/3/19
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course provides the basic tools to analyze micro-data in social sciences with the software for statistical analysis R. This course focuses the basic concepts of statistics (descriptive statistics, test, estimation, cross table, variance analysis, correlation, regression, logistic regression, etc.). Through this course, students learn how interpret results of analysis in social science.
Student learning outcomes
By the end of this course, students will be able to:
1) Understand the basic tools to analyze microdata in social sciences.
2) Analyze microdata with R.
3) Provide presentation their result of analysis.
Keywords
Quantitative sociology, Statistics, Software for statistical analysis R
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Computer exercise
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Quantitative analysis in social sciences | Understand the nature of quantitative analysis in social sciences. |
| Class 2 | Descriptive statistics | Understand descriptive statistics and acquire analytical skills. |
| Class 3 | Estimation and test | Understand estimation and test, and acquire analytical skills. |
| Class 4 | Cross table | Understand cross table and acquire analytical skills. |
| Class 5 | Analysis of Variance | Understand Analysis of Variance and acquire analytical skills. |
| Class 6 | Correlation | Understand correlation and acquire analytical skills. |
| Class 7 | Regression: single and multiple regression analysis | Understand regression and acquire analytical skills. |
| Class 8 | Regression: dummy variable and interaction | Understand dummy variable and interaction in regression. |
| Class 9 | Logistic regression | Understand logistic regression and acquire analytical skills. |
| Class 10 | Ordered and multinomial logistic regression | Understand ordered and multinomial logistic regression and acquire analytical skills. |
| Class 11 | Tips for Oral presentation with statistics analysis | Prepare the oral presentation. |
| Class 12 | Presentation | Presentation |
| Class 13 | Presentation | Presentation |
| Class 14 | Presentation | Presentation |
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 required.
Reference books, course materials, etc.
Materials will be specified by the instructor.
Assessment criteria and methods
Students' course scores are based on class reports 50% and the final presentation 50% (35% from teacher and 15% from other students).
Related courses
- LAH.T101 ： Statistics A
- LAH.T201 ： Statistics B
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students require the basic knowledge of statistics.
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
kkezuka[at]ila.titech.ac.jp
Other
Should the number of applicants for this course exceed the limit, a lottery system will be used to determine which students are admitted. Be sure to attend the first class.
