RMSE is the root-mean-square error, defined as sqrt(mean(e_1^2)) where e_1 is the list of residuals of the regression. The RMSE is used in cases where there is not a clear distinction between the independent and dependent variables in a model. Depending on the form of the model, there are 3 different statistics that might be shown:
The Pearson correlation coefficient, r: appropriate when regressing a linear model with both slope and intercept, e.g. y_1 ~ m x_1 + b
The coefficient of determination, R^2: appropriate for non-linear models of the form y_1 ~ f(x_1, x_2, ...)
The root-mean-square error: appropriate for general models of the form f(y_1) ~ g(x_1), or h(x_1, y_1) ~ 0.