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.
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.
Slides, videos, and application exercises
Modelling data
Classification and model building
Model validation
Uncertainty quantification
Labs
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
Lab 12: Smoking while pregnant
Constructing confidence intervals, conducting hypothesis tests, and interpreting results in context of the data
Homework assignments
HW 7: Bike rentals in DC
Exploratory data analysis and fitting and interpreting models