With regard to a regression-based forecast, the standard error of the estimate gives a measure of A. the variability around the regression line. B. the time required to derive the forecast equation. C. the maximum error of the forecast. D. the time period for which the forecast is valid.

B. the variability around the regression line.

Step-by-step explanation:

The standard errors represents the distance (how sparse) the observed values fall from the regression line.

Standard errors for regression are measures of the spread of variables around the average (regression line)

The standard error is dependent on the standard deviation of the observations and the reliability of the test.

When the test is perfectly reliable, the standard error is zero and when unreliable, it is equal to the standard deviation of the observations.