Statistical Methods in Environmental Studies
A.Y. 2021/2022
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
Detailed information on the delivery modes of training activities for academic year 2021/2022 will be provided over the coming months, based on the evolution of the public health situation.
Course syllabus
Environmental sampling.
Statistical models (discrete and continuous distributions, Bayesian analysis, frequentist procedures).
Regression-type models and methods.
Hierarchical models.
Time series and forecasting.
Spatial modeling (point-referenced and areal data, Kriging).
Statistical models (discrete and continuous distributions, Bayesian analysis, frequentist procedures).
Regression-type models and methods.
Hierarchical models.
Time series and forecasting.
Spatial modeling (point-referenced and areal data, Kriging).
Prerequisites for admission
Prerequisites for this course include a good knowledge of the mathematical tools presented in Calculus I, Linear Algebra and Basic Statistics courses (crash course)
Teaching methods
Face-to-face lectures, tutorials
Teaching Resources
V. Barnett, Environmental Statistics. Methods and Applications. Wiley, 2004.
Xiaofeng Wang, Yuryan Yue, Julian J. Faraway. Bayesian Regression Modeling with INLA. Chapman & Hall/CRC, 2018.
Lecture Notes.
Xiaofeng Wang, Yuryan Yue, Julian J. Faraway. Bayesian Regression Modeling with INLA. Chapman & Hall/CRC, 2018.
Lecture Notes.
Assessment methods and Criteria
Written Exam
MED/01 - MEDICAL STATISTICS
SECS-P/05 - ECONOMETRICS
SECS-P/05 - ECONOMETRICS
Practicals: 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