Third, the distance between each individual data point and the regression line is the error in prediction—a direct reflection of the correlation between the two variables. For example, if you look at data point (3.3, 3.7), marked in Figure 16.4, you can see that this (X, Y) data point is above the regression line. The distance between that point and the line is the error in prediction, as marked in Figure 16.4, because if the prediction were perfect, then all the predicted points would fall where? Right on the regression or prediction line.
