                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.018
Model:                                          OLS   Adj. R-squared:                  0.018
Method:                               Least Squares   F-statistic:                 1.166e+04
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:53   Log-Likelihood:                 34045.
No. Observations:                            621929   AIC:                        -6.809e+04
Df Residuals:                                621927   BIC:                        -6.806e+04
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1305      0.000    449.326      0.000       0.130       0.131
mean bayes-corrected (q=0.25) valence of replies    -0.0314      0.000   -107.983      0.000      -0.032      -0.031
==============================================================================
Omnibus:                    78154.602   Durbin-Watson:                   1.733
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            31765.731
Skew:                          -0.357   Prob(JB):                         0.00
Kurtosis:                       2.155   Cond. No.                         1.00
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.