                            OLS Regression Results                            
==============================================================================
Dep. Variable:                valence   R-squared:                       0.010
Model:                            OLS   Adj. R-squared:                  0.010
Method:                 Least Squares   F-statistic:                 2.337e+04
Date:                Mon, 22 Jul 2024   Prob (F-statistic):               0.00
Time:                        09:31:53   Log-Likelihood:            -4.8218e+05
No. Observations:             2392896   AIC:                         9.644e+05
Df Residuals:                 2392894   BIC:                         9.644e+05
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===========================================================================================
                              coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------------------
const                       0.1158      0.000    604.951      0.000       0.115       0.116
mean valence of replies    -0.0293      0.000   -152.877      0.000      -0.030      -0.029
==============================================================================
Omnibus:                   785394.853   Durbin-Watson:                   1.750
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           152455.997
Skew:                          -0.323   Prob(JB):                         0.00
Kurtosis:                       1.946   Cond. No.                         1.00
==============================================================================

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