Making rigorous conclusions
In this part we introduce modelling and statistical inference for making data-based conclusions.
We discuss building, interpreting, and selecting models, visualizing interaction effects, and prediction and model validation.
Statistical inference is introduced from a simulation based perspective, and the Central Limit Theorem is discussed very briefly to lay the foundation for future coursework in statistics.
Modelling data
Unit 4 - Deck 1: The language of models
Unit 4 - Deck 2: Fitting and interpreting models
Unit 4 - Deck 3: Modelling nonlinear relationships
Unit 4 - Deck 4: Models with multiple predictors
Unit 4 - Deck 5: More models with multiple predictors
Classification and model building
Unit 4 - Deck 6: Logistic regression
Unit 4 - Deck 7: Prediction and overfitting
Unit 4 - Deck 8: Feature engineering
Model validation
Unit 4 - Deck 9: Cross validation
Uncertainty quantification
Unit 4 - Deck 10: Quantifying uncertainty
Unit 4 - Deck 11: Bootstrapping
Unit 4 - Deck 12: Hypothesis testing
Unit 4 - Deck 13: Inference overview