Computer lab excercise in the Global Change Geography Master programme at HU Berlin.
The students can describe, explain and systematise different advanced statistical and mathematical approaches to the quantitative analysis of geo- and environmental data and the modelling of human-environment systems, e.g. methods of applied and multivariate statistics, mathematical modelling and time series analysis. On the basis of the acquired theoretical and exemplified knowledge, the students can apply existing approaches independently and adapt them to specific problems where necessary. They can develop scientific research questions in the fields of data analysis and modelling and, using the acquired applied programming skills, plan and implement their own analyses.
- Introduction to scientific programming
- R Studio IDE
- Introduction to environmental modelling
- Mathematical preliminaries
- Parameter estimation & linear regression
- ANCOVA, multiple linear regression, dummy coding, collinearity, over-parameterisation, model comparison
- Generalised Linear Models (logistic & log-linear)
- Principle Component Analysis (PCA), Multivariate ANOVA (MANOVA), Discriminant Function Analysis (DFA)
- Measures of accuracy, confusion matrix, ROC/AUC, cross-validation; cluster analysis (kmeans & hierarchical)
- Introduction to spatial statistics
- Spatial autocorrelation
- Spatial weights and linear modelling