R-Squared ($R^2$)—the proportion of variance explained—measures how well a prediction model for "regression" fits the given data. It is independent of the scale of $Y$. $0 \leq R^2 \leq 1$ The larger the R-Squared value, the closer it is to 1, the better the regression model explains the variation of observations around its mean. $R^2 = \frac{TSS-RSS}{TSS} = 1 - \frac{RSS}{TSS} = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2}$