Week Lecture PCLab
1 Dies academicus Introduction to R
2 Introductions Data manipulation and import/export with R
3 Mathematical preliminaries Visualization and data manipulation with R
4 The linear model Linear regression
5 Categorical variables (ANOVA) and dummy coding Hypothesis testing and ANOVA
6 Multiple linear regression Multiple linear regression
7 Maximum Likelihood and outlook to Bayesian statistics Machine learning
8 Generalized linear models I Generalized linear models I
9 Generalized linear models II Generalized linear models II
10 Multivariate methods I Principal component analysis
11 Multivariate methods II Discriminant function analysis and Model validation
12 Understanding spatial data Spatial data and cluster analysis in R
13 Point pattern analysis Point pattern analysis and spatial auto-correlation
14 Spatial autocorrelation and interpolation Semivariogram analysis and kriging
15 Spatial weights and linear modeling Spatial regression models

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