                                   OLS Regression Results                                   
============================================================================================
Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.032
Model:                                          OLS   Adj. R-squared:                  0.032
Method:                               Least Squares   F-statistic:                     3344.
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:54   Log-Likelihood:                -6723.8
No. Observations:                            100071   AIC:                         1.345e+04
Df Residuals:                                100069   BIC:                         1.347e+04
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1246      0.001    152.318      0.000       0.123       0.126
mean bayes-corrected (q=0.25) valence of replies    -0.0473      0.001    -57.827      0.000      -0.049      -0.046
==============================================================================
Omnibus:                    28267.899   Durbin-Watson:                   1.740
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6368.870
Skew:                          -0.345   Prob(JB):                         0.00
Kurtosis:                       1.975   Cond. No.                         1.00
==============================================================================

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