                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.043
Model:                                          OLS   Adj. R-squared:                  0.043
Method:                               Least Squares   F-statistic:                     1211.
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):          1.61e-259
Time:                                      09:31:54   Log-Likelihood:                -439.22
No. Observations:                             27005   AIC:                             882.4
Df Residuals:                                 27003   BIC:                             898.9
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1074      0.001     71.776      0.000       0.104       0.110
mean bayes-corrected (q=0.25) valence of replies    -0.0521      0.001    -34.794      0.000      -0.055      -0.049
==============================================================================
Omnibus:                     6746.889   Durbin-Watson:                   1.761
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1324.435
Skew:                          -0.197   Prob(JB):                    2.53e-288
Kurtosis:                       1.989   Cond. No.                         1.00
==============================================================================

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