Regardless of the model, you can always compute the total variance of the dependent variable (total sum of squares, SST), the proportion of variance due to the residuals (error sum of squares, SSE), and the proportion of variance due to the regression model (regression sum of squares, SSR=SST-SSE). The ratio of the regression sum of squares to the total sum of squares (SSR/SST) explains the proportion of variance accounted for in the dependent variable (y) by the model; thus, this ratio is equivalent to the R-square (0 ≤ R-square ≤ 1, the coefficient of determination). Even when the dependent variable is not normally distributed across cases, this measure may help evaluate how well the model fits the data.