Residual Standard Error is an estimate of σ standard deviation. It assesses the quality of linear regression. It is the average deviation of a response from the true regression line.

RSE is considered a measure of lack of fit. So if it is large, the model is considered not to fit well whereas if RSE is small, the model may fit well.

$RSE = \sqrt{\dfrac{RSS}{n-2}}= \sqrt{\dfrac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{n-2}}$ Where n − 2 is the degrees of freedom.