2017 Environmental Statistics

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Academic unit or major
Graduate major in Civil Engineering
Instructor(s)
Yoshimura Chihiro 
Course component(s)
Lecture
Day/Period(Room No.)
Tue5-6(M112)  Fri5-6(M112)  
Group
-
Course number
CVE.G402
Credits
2
Academic year
2017
Offered quarter
4Q
Syllabus updated
2017/3/17
Lecture notes updated
2018/1/26
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 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

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, Machine learning, Monte-Carlo Method

Competencies that will be developed

Intercultural skills Communication skills Specialist 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/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 Understand probability distribution for environmental processes and work on its exercise
Class 3 Hypothesis test Understand concept and basic skills of hypothesis test and work on its exercise
Class 4 t-test and data transformation Understand t-test and data transformation and work on its exercise
Class 5 Simple regression analysis Understand simple regression analysisand work on its exercise
Class 6 Multiple regression analysis and other statistical models Understand multiple regression analysis and other statistical models and work on exercise
Class 7 Analysis of variance (ANOVA) Understand analysis of variance (ANOVA) and work on its exercise
Class 8 Mid-term exercise Review major statical methods for hypothesis test and work on its exercise
Class 9 Multivariate exploratory technique (1) Ordination, principle component analysis Understand ordination and principle component analysisand work on its exercise
Class 10 Multivariate exploratory technique (2) Cluster analysis Understand cluster analysis and work on its exercise
Class 11 Diversity measure Understand diversity measure and work on its exercise
Class 12 Nonparametric analysis Understand nonparametric analysis and work on its exercise
Class 13 Time series analysis Understand time series analysis and work on its exercise
Class 14 Machine leaning Understand major machine leaning algorithm and work on its exercise
Class 15 Monte-Carlo method and risk assessment Understand Monte-Carlo method and risk assessmentand work on its exercise

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 (including reports) 70%
Discussion 30%
Students are required to attend more than 9 times out of 15 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

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