**Learning objectives**

- How a model partitions variation
- Definition of R squared and correlation
- Geometry of data and models. Every model coefficient corresponds to an explanatory model vector.

**Before class**

- Read Chapter 9
- Complete cars project and submit link

**In class**

- Geometry of a data frame. Way 1, case-by-case: Plot each case as a point on a coordinate system with 1 axis for each variable. Way 2, variable-by-variable: Plot each variable as a vector on a coordinate system with 1 axis for each case.
- Counting model vectors. A linear model determines a set of explanatory vectors.
- What is Rsquared for a linear model?
- Quiz 5 (on models and their coefficients.)