                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.028
Model:                                          OLS   Adj. R-squared:                  0.028
Method:                               Least Squares   F-statistic:                     6790.
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:54   Log-Likelihood:                -13916.
No. Observations:                            235551   AIC:                         2.784e+04
Df Residuals:                                235549   BIC:                         2.786e+04
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1362      0.001    257.499      0.000       0.135       0.137
mean bayes-corrected (q=0.25) valence of replies    -0.0436      0.001    -82.403      0.000      -0.045      -0.043
==============================================================================
Omnibus:                    52959.753   Durbin-Watson:                   1.732
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            15867.344
Skew:                          -0.409   Prob(JB):                         0.00
Kurtosis:                       2.027   Cond. No.                         1.00
==============================================================================

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