In regression analysis, we judge the magnitude of the standard error of estimate relative to the values of the dependent variable, and particularly to the mean of y.

Question

Quizplus · Accepted Answer

This is true. The standard error of estimate is a measure of the variability of the residuals, or the differences between the predicted and actual values of the dependent variable. By comparing the standard error of estimate to the mean of y, we can get a sense of how much variability there is in the regression model relative to the overall scale of the dependent variable. If the standard error of estimate is small compared to the mean of y, it suggests that the model is a good fit and that the predicted values are close to the actual values. If the standard error of estimate is large relative to the mean of y, it suggests that there is a lot of variability in the model and that the predicted values may not be very accurate.

In Regression Analysis, We Judge the Magnitude of the Standard