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School of Engineering
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- Common courses
- School of Materials and Chemical Technology
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- School of Life Science and Technology
- School of Environment and Society
- 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
- Graduate major in Civil Engineering
- Instructor(s)
- Yoshimura Chihiro
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Mon3-4(S2-203(S222）) Thr3-4(W5-106)
- Group
- -
- Course number
- CVE.G402
- Credits
- 2
- Academic year
- 2024
- Offered quarter
- 1Q
- Syllabus updated
- 2024/3/14
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
This course provides students with common statistical skills to analyze and interpret data sets obtained in environmental science and environmental management. Main topics are probability, hypothesis testing, multivariate analysis, time series analysis, and risk assessment. Students are required to work on exercises to acquire substantial understanding both in theory and application.
Student learning outcomes
Through this course, students will be able to:
1. Explain major statistical analysis and modeling techniques for scientific understanding of environmental problems.
2. Select appropriate statistical methods depending on a purpose of data analysis.
3. Apply major statistical analysis and modeling techniques to particular dataset, and interpret the results from such applications.
Keywords
Hypothesis Test, Regression Analysis, Sampling and Experimental Design, Multivariate exploratory technique, Empirical model, Machine learning, Community analysis, and Monte-Carlo Method
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Students are required to work on exercises in every class to promote theoretical and practical understanding, using R (programming language) for exercises.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Guidance Importance of statistics in environmental science and engineering, and role of hypothesis | Understand the importance of statistics in environmental science and engineering, and role of hypothesis |
| Class 2 | Probability distribution and data transformation | Understand probability distribution and data transformation for understanding environmental processes and work on its exercise |
| Class 3 | t-test | Understand statistical test, in particular t-test, and work on its exercise |
| Class 4 | Analysis of variance (ANOVA) | Understand analysis of variance (ANOVA) and work on its exercise |
| Class 5 | Correlation analysis | Understand correlation analysis and work on its exercise |
| Class 6 | Multiple regression analysis | Understand multiple regression analysis and work on exercise |
| Class 7 | Mid-term exercise | Review major statistical methods for hypothesis test and work on its exercise |
| Class 8 | Regression models | Understand major regression models and those application methods, and work on exercise |
| Class 9 | Time series analysis | Understand time series analysis and work on its exercise |
| Class 10 | Bayesian inference and machine leaning | Understand major concepts of Bayesian inference and machine leaning and work on its exercise |
| Class 11 | Multivariate exploratory technique (1) Ordination, principle component analysis | Understand ordination and principle component analysis and work on its exercise |
| Class 12 | Multivariate exploratory technique (2) Cluster analysis | Understand cluster analysis and work on its exercise |
| Class 13 | Evaluation of biodiversity | Understand biodiversity and its quantification methods, and work on its exercise |
| Class 14 | Risk assessment and Monte-Carlo method | Understand Monte-Carlo method and risk assessment and work on its exercise |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are supposed to work on exercises provided in each class.
Textbook(s)
Not specified
Reference books, course materials, etc.
Modern Statistics for the Life Science, 2002, A. Grafen and R. Hails, Oxford University Press
Biostatistical Analysis, 1999, J. H. Zar, Prentice Hall
Multivariate Statistics for the Environmental Sciences, 2003, P. J. A. Shaw, Hodder Arnold
Environmental and Ecological Statistics with R, 2010, S. S. Quin, CRC Press
Assessment criteria and methods
Exercise (assingments) 80%
Feedback 20%
Students are required to attend more than 8 times out of 14 lectures.
Related courses
- CVE.G401 ： Aquatic Environmental Science
- CVE.G310 ： Water Environmental Engineering
- CVE.B311 ： River Engineering
- CVE.B310 ： Coastal Engineering and Oceanography
- CVE.B401 ： Water Resource Systems
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites
