Statistical methods in environmental studies
A.A. 2021/2022
Obiettivi formativi
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
Risultati apprendimento attesi
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.
Periodo: Secondo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo semestre
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.
Programma
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).
Prerequisiti
Prerequisites for this course include a good knowledge of the mathematical tools presented in Calculus I, Linear Algebra and Basic Statistics courses (crash course)
Metodi didattici
Face-to-face lectures, tutorials
Materiale di riferimento
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.
Modalità di verifica dell’apprendimento e criteri di valutazione
Written Exam
MED/01 - STATISTICA MEDICA
SECS-P/05 - ECONOMETRIA
SECS-P/05 - ECONOMETRIA
Esercitazioni: 32 ore
Lezioni: 32 ore
Lezioni: 32 ore
Docente:
Stefanini Federico Mattia
Docente/i
Ricevimento:
Su appuntamento Martedì e Mercoledì (email)
via Celoria 10