Sum-of-squares as a measure of goodness-of-fit in nonlinear regression

The goal of nonlinear regression is to adjust the values of the variables in the model to find the curve that best predicts Y from X. More simply, the goal is to find the curve that comes closest to the points. But how exactly does one assess how close a curve is to your data. To do this, the regression procedure minimizes the sum of the squares of the vertical distances of the points from the curve. For this reason, linear and nonlinear regression are sometimes called least squares methods. Why minimize the sum of the squares of the distances?

Why not simply minimize the sum of the actual distances?If the random scatter follows a Gaussian distribution, it is far more likely to have two medium size deviations (say 5 units each) than to have one small deviation (1 unit) and one large (9 units). A procedure that minimized the sum of the absolute value of the distances would have no preference over a curve that was 5 units away from two points and one that was 1 unit away from one point and 9 units from another. The sum of the distances (more precisely, the sum of the absolute value of the distances) is 10 units in each case. A procedure that minimizes the sum of the squares of the distances prefers to be 5 units away from two points (sum-of-squares = 50) rather than 1 unit away from one point and 9 units away from another (sum-of-squares = 82). If the scatter is Gaussian (or nearly so), the curve determined by minimizing the sum-of-squares is most likely to be correct.