                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.057
Model:                                          OLS   Adj. R-squared:                  0.057
Method:                               Least Squares   F-statistic:                 9.915e+04
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:54   Log-Likelihood:             2.1429e+05
No. Observations:                           1630262   AIC:                        -4.286e+05
Df Residuals:                               1630260   BIC:                        -4.286e+05
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1419      0.000    854.072      0.000       0.142       0.142
mean bayes-corrected (q=0.25) valence of replies    -0.0523      0.000   -314.877      0.000      -0.053      -0.052
==============================================================================
Omnibus:                   101738.374   Durbin-Watson:                   1.753
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            62821.338
Skew:                          -0.351   Prob(JB):                         0.00
Kurtosis:                       2.343   Cond. No.                         1.00
==============================================================================

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