                                   OLS Regression Results                                  
===========================================================================================
Dep. Variable:     bayes-corrected (q=0.5) valence   R-squared:                       0.027
Model:                                         OLS   Adj. R-squared:                  0.027
Method:                              Least Squares   F-statistic:                 6.556e+04
Date:                             Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                                     09:31:52   Log-Likelihood:             3.9215e+05
No. Observations:                          2392896   AIC:                        -7.843e+05
Df Residuals:                              2392894   BIC:                        -7.843e+05
Df Model:                                        1                                         
Covariance Type:                         nonrobust                                         
===================================================================================================================
                                                      coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------------------------------------------
const                                               0.1323      0.000    996.732      0.000       0.132       0.133
mean bayes-corrected (q=0.5) valence of replies    -0.0340      0.000   -256.042      0.000      -0.034      -0.034
==============================================================================
Omnibus:                   168653.316   Durbin-Watson:                   1.726
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           107460.980
Skew:                          -0.396   Prob(JB):                         0.00
Kurtosis:                       2.328   Cond. No.                         1.00
==============================================================================

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