The standard error of the estimate is a measure of the accuracy of predictions made with a regression line. Consider the following data.

The second column (Y) is predicted by the first column (X). The slope and Y intercept of the regression line are 3.2716 and 7.1526 respectively. The third column, (Y'), contains the predictions and is computed according to the formula:

Y' = 3.2716X + 7.1526.

The fourth column (Y-Y') is the error of prediction. It is simply the difference between what a subject's actual score was (Y) and what the predicted score is (Y').

The sum of the errors of prediction is zero. The last column, (Y-Y')², contains the squared errors of prediction.