Residual Standard Deviation

The residual standard deviation is a measure of the dispersion of the residuals (prediction errors) from a regression model. It is calculated as the square root of the mean squared error (MSE).

The MSE is a measure of how well the regression model fits the data. A low MSE indicates that the model is a good fit for the data, while a high MSE indicates that the model is not a good fit.

The residual standard deviation is a more interpretable measure of the dispersion of the residuals than the MSE. It is expressed in the same units as the dependent variable, so it is easier to compare the residual standard deviation across different regression models.

The residual standard deviation can be used to evaluate the performance of a regression model. A low residual standard deviation indicates that the model is a good fit for the data, while a high residual standard deviation indicates that the model is not a good fit.

The residual standard deviation can also be used to compare different regression models. A model with a lower residual standard deviation is a better fit for the data than a model with a higher residual standard deviation.

The residual standard deviation is a useful tool for evaluating the performance of regression models. It is a more interpretable measure of the dispersion of the residuals than the MSE, and it can be used to compare different regression models.