                                   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:                     1805.
Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                      09:31:54   Log-Likelihood:                -3477.5
No. Observations:                             63079   AIC:                             6959.
Df Residuals:                                 63077   BIC:                             6977.
Df Model:                                         1                                         
Covariance Type:                          nonrobust                                         
====================================================================================================================
                                                       coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------
const                                                0.1191      0.001    117.001      0.000       0.117       0.121
mean bayes-corrected (q=0.25) valence of replies    -0.0433      0.001    -42.490      0.000      -0.045      -0.041
==============================================================================
Omnibus:                    21454.701   Durbin-Watson:                   1.721
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4028.801
Skew:                          -0.319   Prob(JB):                         0.00
Kurtosis:                       1.939   Cond. No.                         1.00
==============================================================================

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