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The page below is a sample from the LabCE course Linear Regression Analysis. Access the complete course and earn ASCLS P.A.C.E.-approved continuing education credits by subscribing online.

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Standard Error of Estimate

The sum of squares of the deviations can also be used to provide an estimate of how closely the data cluster around the line. The standard error of estimate (Se) is a measure of the accuracy of predictions. It is calculated according to the following formula, where S denotes summation, and the yi and i are the observed and theoretical y-values of the data points, respectively.

For example, the Standard Error of Estimate Se for line A in the preceding example is the following:
    for line B, the Se is 12.114
    For line C, the Se is 17.656.
The smaller the standard error of estimate in a regression line, the more accurate the prediction.