                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.024
Model:                                          OLS   Adj. R-squared:                  0.024
Method:                               Least Squares   F-statistic:                     1726.
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:54   Log-Likelihood:                 10825.
No. Observations:                             69253   AIC:                        -2.165e+04
Df Residuals:                                 69251   BIC:                        -2.163e+04
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1109      0.001    141.050      0.000       0.109       0.112
mean bayes-corrected (q=0.25) valence of replies    -0.0327      0.001    -41.551      0.000      -0.034      -0.031
==============================================================================
Omnibus:                     6922.840   Durbin-Watson:                   1.814
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2381.203
Skew:                          -0.195   Prob(JB):                         0.00
Kurtosis:                       2.179   Cond. No.                         1.00
==============================================================================

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