                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.019
Model:                                          OLS   Adj. R-squared:                  0.019
Method:                               Least Squares   F-statistic:                     8343.
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:53   Log-Likelihood:                -43060.
No. Observations:                            440260   AIC:                         8.612e+04
Df Residuals:                                440258   BIC:                         8.615e+04
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1353      0.000    336.404      0.000       0.134       0.136
mean bayes-corrected (q=0.25) valence of replies    -0.0367      0.000    -91.341      0.000      -0.038      -0.036
==============================================================================
Omnibus:                   129058.321   Durbin-Watson:                   1.735
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32315.635
Skew:                          -0.421   Prob(JB):                         0.00
Kurtosis:                       1.974   Cond. No.                         1.00
==============================================================================

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