Standard Error of the Estimate
Prerequisites
Measures
of Variability,
Introduction to Simple Linear Regression, Partitioning Sums of
Squares
Learning Objectives
 Make judgments about the size of the standard error of the estimate
from a scatterplot
 Compute the standard error of the estimate based on errors of prediction
 Compute the standard error using Pearson's correlation
 Estimate the standard error of the estimate based on a sample
The
standard error of the estimate is a measure of the accuracy
of predictions. Recall that the regression line is the line that
minimizes the sum of squared deviations of prediction (also
called the sum
of squares error). The standard error of the estimate
is closely related to this quantity and is defined below:
where sest is
the standard error of the estimate, Y is an actual score, Y' is
a predicted score, and N is the number of pairs of scores. The
numerator is the sum of squared differences between
the actual scores and the predicted scores. Assume the
data in Table 1 are the data from a population of five XY pairs.
The last column shows that the sum of the squared
errors of prediction is 2.791. Therefore, the standard error of
the estimate is
There is a version of the formula for the standard error in terms
of Pearson's correlation:
where r is the population
value of Pearson's correlation and SSY is
For the data in Table 1, my =
10.30, SSY = 4.597 and r = 0.6268.
Therefore,
which is the same value computed previously.
Similar formulas are used when the standard error
of the estimate is computed from a sample rather than a population.
The only difference is that the denominator is N2 rather than
N. The reason N2 rather than N1 is used is that two parameters
(the slope and the intercept) were estimated in order to estimate
the sum of squares. Formulas comparable to the ones for the population
are shown below.
