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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