Statistical Methods in Environmental Studies
A.Y. 2026/2027
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 cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Common families of probability distributions. Joint distributions of multiple random variables, marginalization, and conditioning. Introduction to conditional independence and Bayesian networks. Statistical inference and computation: environmental sampling designs, the likelihood principle, and the Bayesian paradigm. Linear and generalized linear models, including multiple regression, Poisson regression, and logistic regression. Censored data. Environmental monitoring, impact assessment, and reclamation assessment. Introduction to time series analysis and forecasting. Introduction to spatial data analysis.
Prerequisites for admission
Students should be familiar with basic concepts of matrix algebra and calculus, as well as with introductory probability and descriptive statistics. They should possess basic computer skills and be able to use at least one of the following operating systems: Windows, macOS, or Linux.
Teaching methods
The course consists of face-to-face lectures on statistical concepts and practical computer-lab sessions using R and RStudio (free software). During the practical sessions, students will be encouraged to develop R code based on the annotated examples and apply it to solve problem sets using either a laboratory computer or their own laptop. Up to 10% of the course is delivered online, depending on the presentation of specific R packages and case studies implemented in R. Class attendance is not compulsory, but it is strongly recommended.
Teaching Resources
Main study materials: course notes and online resources (course website).
Supplementary materials:
- Manly B.F.J, 2008, Statistics for Environmental Science and Management, CRC Press. https://doi.org/10.1201/9781439878125.
- Qian S.S., DuFour M.R., Alameddine I., 2022, Bayesian Applications in Environmental and Ecological Studies with R and Stan. CRC Press. https://doi.org/10.1201/9781351018784.
- Lohr S.L., 2022, Sampling. Design and Analysis, CRC Press, https://doi.org/10.1201/9780429298899.
- M. Scutari and J.B. Denis, 2021, Bayesian Networks with Examples in R, Chapman & Hall/CRC, 2nd edition.
ISBN-10: 0367366517, ISBN-13: 978-0367366513.
Supplementary materials:
- Manly B.F.J, 2008, Statistics for Environmental Science and Management, CRC Press. https://doi.org/10.1201/9781439878125.
- Qian S.S., DuFour M.R., Alameddine I., 2022, Bayesian Applications in Environmental and Ecological Studies with R and Stan. CRC Press. https://doi.org/10.1201/9781351018784.
- Lohr S.L., 2022, Sampling. Design and Analysis, CRC Press, https://doi.org/10.1201/9780429298899.
- M. Scutari and J.B. Denis, 2021, Bayesian Networks with Examples in R, Chapman & Hall/CRC, 2nd edition.
ISBN-10: 0367366517, ISBN-13: 978-0367366513.
Assessment methods and Criteria
The main purpose of the written examination is to assess the achievement of the learning objectives, including the ability to identify an appropriate statistical model to address a research question, perform the corresponding analysis using R, and correctly interpret the R output to support decision-making.
The examination (1 hour) consists of a written test comprising up to five open-ended questions (typically graded within the range [-1, 3] points each) and up to five multiple-choice questions (graded within the range [-1, 2] or [-1, 3] points each, depending on the question). The questions cover theoretical concepts, case studies, and the interpretation of R software output. The grade is reported on a 30-point scale. The instructor may require the student to complete the examination orally, depending on the content of the written test. In such cases, a single overall grade will be awarded.
Students are required to bring only a pen. The use of a pocket calculator is permitted, provided that it is not capable of remote communication. Mobile phones, tablets, laptops, smart glasses, earphones, smartwatches, and any other connected devices are strictly prohibited.
The examination (1 hour) consists of a written test comprising up to five open-ended questions (typically graded within the range [-1, 3] points each) and up to five multiple-choice questions (graded within the range [-1, 2] or [-1, 3] points each, depending on the question). The questions cover theoretical concepts, case studies, and the interpretation of R software output. The grade is reported on a 30-point scale. The instructor may require the student to complete the examination orally, depending on the content of the written test. In such cases, a single overall grade will be awarded.
Students are required to bring only a pen. The use of a pocket calculator is permitted, provided that it is not capable of remote communication. Mobile phones, tablets, laptops, smart glasses, earphones, smartwatches, and any other connected devices are strictly prohibited.
ECON-05/A - Econometrics - University credits: 1
STAT-01/A - Statistics - University credits: 5
STAT-01/A - Statistics - University credits: 5
Exercises: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Stefanini Federico Mattia
Professor(s)
Reception:
by appointment on Tuesday and Wednesday (email)
Via Celoria 10