Residual standard error of a regression in python

Question

Does anyone know a command or a way to obtain the residual standard error of a regression or standard error of a regression?

I use the following commands to get the coefficients and the R-squared, I would like to learn one command like these for the standard error of the regression:

#For the coefficients:
model = smf.OLS(y, X).fit()
print(model.params)

#For the R-squared:
model = smf.OLS(y, X).fit()
print(model.rsquared)

I will be really grateful to the person that can help me with this issue.

Answer 1

For a smf.ols fit

# Residual Standard Error of the model
np.sqrt(fitted_model.scale)

Answer 2

To get Residual Standard Error (RSE) of a regression model in python's statsmodels library, you can simply apply the standard deviation method with the degree of freedom equal to the number of predictors (p) + 1 as below:

model = sm.OLS(y, X).fit()
model.resid.std(ddof=X.shape[1])