When learning about linear models —that is, regression, ANOVA, and similar techniques—we are taught to calculate an R^{2}. The R^{2} has the following useful properties:

- The range is limited to [0,1], so we can easily judge how relatively large it is.
- It is standardized, meaning its value does not depend on the scale of the variables involved in the analysis.
- The interpretation is pretty clear: It is the proportion of variability in the outcome that can be explained by the independent variables in the model.

The calculation of the R^{2} is also intuitive, once you understand the concepts of variance and prediction. [Read more…] about R-Squared for Mixed Effects Models