                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.021
Model:                                          OLS   Adj. R-squared:                  0.021
Method:                               Least Squares   F-statistic:                 5.020e+04
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:51   Log-Likelihood:                -221.34
No. Observations:                           2392896   AIC:                             446.7
Df Residuals:                               2392894   BIC:                             472.1
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1246      0.000    796.722      0.000       0.124       0.125
mean bayes-corrected (q=0.25) valence of replies    -0.0351      0.000   -224.063      0.000      -0.035      -0.035
==============================================================================
Omnibus:                   426104.077   Durbin-Watson:                   1.729
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           131391.270
Skew:                          -0.336   Prob(JB):                         0.00
Kurtosis:                       2.070   Cond. No.                         1.00
==============================================================================

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