OLS Regression Results ================================================================================= Dep. Variable: number O(n+1)-replies R-squared: 0.256 Model: OLS Adj. R-squared: 0.256 Method: Least Squares F-statistic: 3.965e+04 Date: Mon, 22 Jul 2024 Prob (F-statistic): 0.00 Time: 09:31:48 Log-Likelihood: -3.4768e+05 No. Observations: 230524 AIC: 6.954e+05 Df Residuals: 230521 BIC: 6.954e+05 Df Model: 2 Covariance Type: nonrobust ==================================================================================================== coef std err t P>|t| [0.025 0.975] ---------------------------------------------------------------------------------------------------- const 0.8891 0.002 390.420 0.000 0.885 0.894 bayes-corrected (q=0.25) valence -0.3918 0.002 -171.548 0.000 -0.396 -0.387 totalvotes 0.4784 0.002 209.473 0.000 0.474 0.483 ============================================================================== Omnibus: 109314.794 Durbin-Watson: 1.837 Prob(Omnibus): 0.000 Jarque-Bera (JB): 1540320.347 Skew: 1.926 Prob(JB): 0.00 Kurtosis: 15.063 Cond. No. 1.08 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.