Statistical methods for the environmental research

The course aims to complete and deepen the knowledge already acquired by students in the field of statistics during the three-year degree course, providing concepts and methodologies useful for environmental sciences, with particular attention to univariate statistics, and mentions of multivariate statistics and geostatistics.
The contents of the course will allow students to:
- improve the knowledge of univariate statistics applied to environmental analysis;
- understand which tools are available for the analysis of multivariate phenomena and for spatial analysis;
- understand the fundamental elements of statistical-probabilistic methodologies and their application in the field of spatial and environmental statistics;
- together with the Data Management course, acquire the techniques for surveying, acquiring and managing environmental data and information.

Knowledge and understanding. At the end of the course the students should know:
o univariate statistics applied to spatial analysis: multiple way ANOVA, ANCOVA and regression, with particular attention to the variable selection methods;
o the fundamental elements of multivariate statistics and geostatistics;
o the basic principles of machine learning, with particular attention to neural networks and random forest.
Applying knowledge and understanding. At the end of the course the students should be able to:
o Apply ANOVA and regression to experimental and spatial data, using statistical software;
o correctly choose the most appropriate instruments for their own analysis, based on the possibility and limits of the various approaches available;
o carry out simple multivariate or geostatistical analyses.