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.
The RStudio Cloud workspace for Data Science Course in a Box project is here. You can join the workspace and play around with the sample application exercises.
Unit 4 - Deck 2: Fitting and interpreting models
Unit 4 - Deck 4: Models with multiple predictors
Unit 4 - Deck 9: Cross validation
tidymodels :: Evaluate your model with resampling
Lab 10: Grading the professor, Pt. 1
Fitting and interpreting simple linear regression models
Lab 11: Grading the professor, Pt. 2
Fitting and interpreting multiple linear regression models
HW 7: Bike rentals in DC
Exploratory data analysis and fitting and interpreting models