What is the formula for Residual Standard Error and how is it implemented in Python?

The formula for RSE is: $RSE = \sqrt{\frac{\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}{n-p-1}}$, where $y_i$ is the observed value, $\hat{y}_i$ is the predicted value, $n$ is the number of observations, and $p$ is the number of predictor variables. In Python, you can implement this by first calculating the residuals (`y_true - y_pred`), squaring them, summing them, dividing by `(n - p - 1)`, and then taking the square root. Libraries like `statsmodels` and `scikit-learn` provide direct ways to obtain this value.