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.
teacher profile teaching materials
Programme
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.Prerequisiti
Basic statistics course with basic inference (confidence intervals and tests)Core Documentation
Carsten Dormann (2020) Environmental Data Analysis: An introduction with Examples in R, SpringerReference Bibliography
Carsten Dormann (2020) Environmental Data Analysis: An introduction with Examples in R, SpringerType of delivery of the course
Face-to-face lectures.Attendance
Attendance is not mandatory but strongly adviced.Type of evaluation
Written research project and presentation.