OLS Regression Results Dep. Variable: diabetes R-squared: 0.406 Model: OLS Adj. R-squared: 0.406 Method: Least Squares F-statistic: 1.584e+04 Date: Sat, 05 Feb 2022 Prob (F-statistic): 0.00 Time: 15:19:43 Log-Likelihood: -50201. No. Observations: 23158 AIC: 1.004e+05 Of Residuals: 23156 BIC: 1.004e+05 Df Model: Covariance Type: nonrobust coef std err t P>/t| [0.025 0.975] Intercept 6.5217 0.032 201.240 0.000 6.458 6.585 VULEOPCT 7.9859 0.063 125.848 0.000 7.862 8.110 Omnibus: 2336.483 Durbin-Watson: 0.626 Prob(Omnibus): 0.000 Jarque-Bera (JB): 10590.866 Skew: 0.407 Prob(JB): 0.00 Kurtosis: 6.211 Cond. No. 5.58 Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. Question: Try interpreting the values 6.5217 and 7.9859. What do they tell us about the linear relationship we are regressing