OLS Regression Results ================================================================================= Dep. Variable: number O(n+1)-replies R-squared: 0.209 Model: OLS Adj. R-squared: 0.209 Method: Least Squares F-statistic: 1.708e+05 Date: Mon, 22 Jul 2024 Prob (F-statistic): 0.00 Time: 09:31:48 Log-Likelihood: -2.1743e+06 No. Observations: 1295105 AIC: 4.349e+06 Df Residuals: 1295102 BIC: 4.349e+06 Df Model: 2 Covariance Type: nonrobust ==================================================================================================== coef std err t P>|t| [0.025 0.975] ---------------------------------------------------------------------------------------------------- const 1.1182 0.001 981.264 0.000 1.116 1.120 bayes-corrected (q=0.25) valence -0.3909 0.001 -341.822 0.000 -0.393 -0.389 totalvotes 0.5079 0.001 444.124 0.000 0.506 0.510 ============================================================================== Omnibus: 680589.819 Durbin-Watson: 1.782 Prob(Omnibus): 0.000 Jarque-Bera (JB): 49094495.451 Skew: 1.699 Prob(JB): 0.00 Kurtosis: 32.971 Cond. No. 1.09 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.