Being able to choose the most appropriate statistical model for the analysis of environmental phenomena.
Getting familiar with the statistical environment R for model estimation and goodness of fit evaluation.
Being able to communicate efficiently the model output.
Getting familiar with the statistical environment R for model estimation and goodness of fit evaluation.
Being able to communicate efficiently the model output.
scheda docente
materiale didattico
Programma
Software R-studio and R: installation and main features. Project creation in R studio. First step with R: elementary operations, descriptive statistics, graphs. Statistical inference refresher. Sampling distribution, confidence intervals and tests. Environmental data. Maximum likelihood estimation. Linear models: parameter estimation, model selection and goodness of fit. Generalized linear models: parameter estimation, model selection and goodness of fit. Logistic regression. Poisson regression.Testi Adottati
Carsten Dormann (2020) Environmental Data Analysis: An introduction with Examples in R, SpringerBibliografia Di Riferimento
Carsten Dormann (2020) Environmental Data Analysis: An introduction with Examples in R, SpringerModalità Erogazione
Face-to-face lectures.Modalità Frequenza
Attendance is not mandatory but strongly adviced.Modalità Valutazione
Written research project and presentation.