1 | Introduction to regression | ROS Ch.6 | |

2 | Linear regression with a single predictor | ROS Ch.7 | Ex 6.2, 6.3, 6.6, 7.7, 7.8 |

3 | Fitting linear regression | ROS Ch.8 | |

4 | Prediction and Bayesian inference | ROS Ch.9 | |

5 | Linear regression with multiple predictors | ROS Ch.10 | Ex 8.9, 9.7, 10.6(a), 10.7 |

6 | Model diagnostics and evaluation | ROS Ch.11 | |

7 | Cross validation and log transformations | ROS Ch.11, 12 | |

8 | Comparing regression models | ROS Ch.12 | |

9 | Logistic regression | ROS Ch.13, 14 | Ex 11.5, 12.6(a)-(c), 12.7(a) |

10 | Interactions, APD, diagnostics | ROS Ch.14 | |

11 | Generalized linear models | ROS Ch.15 | |

12 | Basics of causal inference | ROS Ch.18 | |

13 | Causal inference with regression | ROS Ch.19 | Ex 18.1, 18.2, 18.4(a), 18.5 |

14 | Causal inference with observational data | ROS Ch.20 | |

15 | Subclassification and propensity score matching | ROS Ch.20 | |

16 | Instrumental variables | ROS Ch.21.1-2 | |

17 | Regression discontinuity | ROS Ch.21.3 | Pdf and data |

18 | Difference-in-Differences | ROS Ch.21.4 | |

19 | Panel data | ROS Ch.21.4 | |

20 | Synthetic control | | |

21 | Conformal prediction | | |

22 | Jackknife+, CV+, Quantile regression and classification | | |