R2 is the percentage of variation in the response that is explained by the model. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by model) to the total sum of squares (which is the total variation in the model).
Use R2 to determine how well the model fits your data. The higher the R2 value, the more variation in the response values is explained by the model. R2 is always between 0% and 100%.
Consider the following issues when interpreting the R2
Assuming the models have the same covariance structure, R2 increases when you add additional fixed factors or covariates. Therefore, R2 is most useful when you compare models of the same size.
Small samples do not provide a precise estimate of the strength of the relationship between the response and predictors. If you need R2 to be more precise, you should use a larger sample (typically, 40 or more).
R2 is just one measure of how well the model fits the data. Even when a model has a high R2, you should check the residual plots to verify that the model meets the model assumptions.