This is simply the sum of squared errors of the model, that is the sum of squared differences between true values y and corresponding model predictions ŷ. In simple linear least-squares regression, Y ~ aX + b, the coefficient of determination R2 coincides with the square of the Pearson correlation coefficient between x1, …, xn […]
