2025 (Current Year) Faculty Courses School of Environment and Society Department of Civil and Environmental Engineering Graduate major in Civil Engineering
Environmental Statistics
- Academic unit or major
- Graduate major in Civil Engineering
- Instructor(s)
- Chihiro Yoshimura
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Mon / 3-4 Thu
- Class
- -
- Course Code
- CVE.G402
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Mar 19, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (preparation and review)
For 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
Evaluation methods and criteria
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
No prerequisites