Print 'std err' value from statsmodels OLS results

Applying the answer given here I used dir() to print all the attributes of the results object.

After that I searched for the one that contained the std err value and it turned out to be:

print results.bse

(Not sure what the b stands for in bse, but I guess the se stands for "standard error")

results.bse provides standard errors for the coefficients, identical to those listed in results.summary().

The standard error of the regression is obtained using results.scale**.5.

Also identical to np.sqrt(np.sum(results.resid**2)/results.df_resid), where results is your fitted model.

The following function can be used to get an overview of the regression analysis result. The parameter ols_model is the regression model generated by statsmodels.formula.api. The output is a pandas data frame saving the regression coefficient, standard errors, p values, number of observations, AIC, and adjusted rsquared. The standard errors are saved in brackets. ***, **, and * represent 0.001, 0.01, 0.1 significance level:

def output_regres_result(ols_model, variable_list: list):    """    Create a pandas dataframe saving the regression analysis result    :param ols_model: a linear model containing the regression result.    type: statsmodels.regression.linear_model.RegressionResultsWrapper    :param variable_list: a list of interested variable names    :return: a pandas dataframe saving the regression coefficient, pvalues, standard errors, aic,    number of observations, adjusted r squared    """    coef_dict = ols_model.params.to_dict()  # coefficient dictionary    pval_dict = ols_model.pvalues.to_dict()  # pvalues dictionary    std_error_dict = ols_model.bse.to_dict()  # standard error dictionary    num_observs = np.int(ols_model.nobs) # number of observations    aic_val = round(ols_model.aic, 2) # aic value    adj_rsqured = round(ols_model.rsquared_adj, 3) # adjusted rsqured    info_index = ['Num', 'AIC', 'Adjusted R2']    index_list = variable_list + info_index    for variable in variable_list:        assert variable in coef_dict, 'Something wrong with variable name!'    coef_vals = []    for variable in variable_list:        std_val = std_error_dict[variable]        coef_val = coef_dict[variable]        p_val = pval_dict[variable]        if p_val <= 0.01:            coef_vals.append('{}***({})'.format(round(coef_val, 4), round(std_val, 3)))        elif 0.01 < p_val <= 0.05:            coef_vals.append('{}**({})'.format(round(coef_val, 4), round(std_val, 3)))        elif 0.05 < p_val <= 0.1:            coef_vals.append('{}*({})'.format(round(coef_val, 4), round(std_val, 3)))        else:            coef_vals.append('{}({})'.format(round(coef_val, 4), round(std_val, 3)))    coef_vals.extend([num_observs, aic_val, adj_rsqured])    result_data = pd.DataFrame()    result_data['coef'] = coef_vals    result_data_reindex = result_data.set_index(pd.Index(index_list))    return result_data_reindex