There seems to be a lot of variation in the name given to the parameter which is required here. The term most often used is Standard Error or Standard Error of Estimate and actually refers to the square root of the residual (predicted y minus actual y) mean square. It has also been referred to as: Standard Error of Regression Estimate of Standard Deviation of Error Residual Standard Deviation Root Mean Square Error The term Standard Error is more correctly applied to the standard deviation of the sampling distribution and should apply to the X variable (gradient) and the intercept, as shown in the Excel regression table.
A common measure of the uncertainty associated with a numerical estimate. In a regression analysis, standard errors are often reported with (or below) the coefficient estimates. As a rough rule of thumb, one can be 95% confident that the true coefficient is within ± 2 standard errors of the estimate.
The standard deviation of a statistic; a measure of the "average" spread of the values of the statistic that would be obtained from repeated samples or experiments of the same size.