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.
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.
Hypothesis Test, Regression Analysis, Sampling and Experimental Design, Multivariate exploratory technique, Machine learning, Monte-Carlo Method
|Intercultural skills||Communication skills||Specialist skills||Critical thinking skills||Practical and/or problem-solving skills|
Students are required to work on exercises in every class to promote theoretical and practical understanding.
|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||Understand multiple regression analysis 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||Regression models||Understand major regression models and those application methods, and work on exercise|
|Class 10||Multivariate exploratory technique (1) Ordination, principle component analysis||Understand ordination and principle component analysisand work on its exercise|
|Class 11||Multivariate exploratory technique (2) Cluster analysis||Understand cluster analysis and work on its exercise|
|Class 12||Diversity measure||Understand diversity measure 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|
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
Exercise (including reports) 70%
Students are required to attend more than 9 times out of 15 lectures.