We can think of the standard error of estimate as
A) the standard deviation of the errors that we make when using the regression equation.
B) the standard deviation of Y.
C) the variance of the errors that we would make when using the regression equation.
D) the variance of X .

Question

Quizplus · Accepted Answer

The standard error of estimate represents the variation or dispersion of the observed values around the predicted values in a regression model. It measures the accuracy of the predictions made by the regression model, by calculating the standard deviation of the residuals (or errors) of the regression line. Therefore, option A is the correct choice as it describes the standard deviation of the errors made when using the regression equation. Option B is incorrect because it describes the standard deviation of Y, which is not related to the standard error of estimate. Option C is also incorrect because it describes the variance of the errors, but we need the standard deviation to calculate the standard error of estimate. Option D is irrelevant because it describes the variance of X, which is not related to the standard error of estimate.

We Can Think of the Standard Error of Estimate as