Statistical Methods in Environmental Studies
A.Y. 2023/2024
Learning objectives
This course provides a broad overview of statistical methods and space-time data analysis frequently used in environmental science and studies. The topics covered in this course aim to provide you with the foundation and tools needed to empirically evaluate data
Expected learning outcomes
At the end of the course the student must be able to perform autonomously statistical analyses of environmental data, often having a space and/or time structure. The student must also be able to produce effective reports of the analysis.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
* Probability and random variables.
* Statistical inference and environmental sampling.
* Bayesian models and computation.
* Regression-type models and methods: multiple regression, Poisson and logistic regression.
* Environmental monitoring and impact assessment.
* Introduction to Time series and forecasting.
* Introduction to the Analysis of spatial data: areal and point-referenced data.
* Statistical inference and environmental sampling.
* Bayesian models and computation.
* Regression-type models and methods: multiple regression, Poisson and logistic regression.
* Environmental monitoring and impact assessment.
* Introduction to Time series and forecasting.
* Introduction to the Analysis of spatial data: areal and point-referenced data.
Prerequisites for admission
The students should be familiar with basic concepts of matrix algebra, Calculus I and should have attended a basic course in probability and statistics.
Teaching methods
Face-to-face lectures and practical sessions using R and RStudio (students will be required to solve problem sets in the lab or using their laptops).
Teaching Resources
* Manly B.F.J, 2009, Statistics for Environmental Science and Management, CRC Press.
* Qian S.S., DuFour M.R., Alameddine I., 2022, Bayesian Applications in Environmental and Ecological Studies with R and Stan. CRC Press.
* Course notes and links to online resources (course website)
* Qian S.S., DuFour M.R., Alameddine I., 2022, Bayesian Applications in Environmental and Ecological Studies with R and Stan. CRC Press.
* Course notes and links to online resources (course website)
Assessment methods and Criteria
The main purpose of the written exam is to assess the achievement of the learning objectives, such as the ability to select the appropriate model to answer research questions, to read the output of statistical softwares, to perfom the appropriate analysis, to use statistical models to support decision making. The exam (about 1 hour) consists of a written test with exercises composed of several open and closed questions, both theoretical and/or focused on the output from the R software to be commented.
SECS-P/05 - ECONOMETRICS
SECS-S/01 - STATISTICS
SECS-S/01 - STATISTICS
Practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Stefanini Federico Mattia
Educational website(s)
Professor(s)
Reception:
by appointment on Tuesday and Wednesday (email)
Via Celoria 10