1. ANOVA Table Models can be evaluated by examining variability.
There are three types of variability that are quantified.
Overall variability present in the data (SST)
Variability explained by the model (SSModel)
Error variability that is unexplained (SSE)
SST = SSModel + SSE
Note: T stands for Total variability and E stands for Error or unexplained variability.

2. If model variability (SSModel) is a lot larger than error variability (SSE) then there is evidence that the model is explanatory.
If model variability (SSModel) is similar to error variability (SSE) then there is no evidence that the model is explanatory.

3. ANOVA Table Mean Squares (MS)
A mean square is a sum of squares divided by the degrees of freedom associated with that sum of squares
Degrees of freedom of SST is N-1
Degrees of freedom of SSModel depends on the model and parameters that need to be estimated
Degrees of freedom of SSE is what is left
Recall: SST = SSModel + SSE

4. ANOVA – Theory How important is the model?
The larger the test statistic, the more important the model
Test statistic follows an F distribution