variable in miles and indicate a good strength in their partnership with the response variable. The coefficients demonstrate that factors are highly associated. The multiple regression equation for this scenario is as such: mpg wt hp mpg 1. 090000 -0. 877244 -0.768871 wt -0. 877244 1.000000 0. 636762 hp - 0. 768871 0. 636762 1.000900 To decide if they will build and sell their vehicles to a customer, the automotive manufacturer can follow this model. If you try to build a gas-filled vehicle, you will realize that the vehicles have to be smaller and have fewer fuel, and you have to look about. OLS Regression Results Dep. Variable: mpg R-squared: 0.844 Model: OLS Adj. R-squared: 0.832 Method: Least Squares F-statistic: 73.00 Date: Thu, 03 Dec 2020 Prob (F-statistic): 1.29e-11 Time: 22:13:37 Log-Likelihood: -66.790 No. Observations: 30 AIC: 139.6 Of Residuals: 27 BIC: 143.8 Of Model: 2 Covariance Type: nonrobust coef std err t P /t| [0.025 0.975] Intercept 36.6800 1.527 24.028 0.000 33.555 39.821 wt -3.8322 0.580 -6.613 0.000 -5.021 -2.643 hp -0.0298 0.008 -3.587 0.001 -0.047 -0.013