2021 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
Media-enhanced courses: -
Day of week/Period (Classrooms): 5-6 Tue / 5-6 Fri
Class: -
Course Code: CVE.G402
Number of credits: 200
Course offered: 2021
Offered quarter: 4Q
Syllabus updated: Jul 10, 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 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

By the end of 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 analysis methods depending on particular environmental problem and type of data.
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, 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.

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	Environmental variability and probability distribution	Understand probability distribution and hypothesis text for understanding environmental processes and work on its exercise
Class 3	t-test and data transformation	Understand t-test and data transformation and work on its exercise
Class 4	Correlation analysis	Understand correlation analysis and work on its exercise
Class 5	Multiple regression analysis	Understand multiple regression analysis and work on exercise
Class 6	Analysis of variance (ANOVA)	Understand analysis of variance (ANOVA) and work on its 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	Diversity measure	Understand diversity measure 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)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 60 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

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 (including reports) 70%
Discussion 30%
Students are required to attend more than 9 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