Consider this regression analysis output table. SUMMARY OUTPUT Dependent Variable: Price ($) Regression Statistics Multiple R 0.8869 R square 0.7866 Adjusted R Square 0.7562 Standard Error 1,564.16 Observations 17 ANOVA df SS MS F Significance f Regression 2 1.26E+08 6.31E+07 25.81 0.0000 Residual 14 3.43E+07 2.45E+06 Total 16 1.61E+08 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -780,540.27 166,349.27 -4.69 0.0003 -1,137,323.97 -423,756.57 Year 395.20 83.12 4.75 0.0003 216.92 573.48 Mileage -0.05 0.01 -5.47 0.0001 -0.07 -0.03 A used car sales manager wants to predict the selling price of cars based on the car's year and its mileage. Which of these statements accurately describes this relationship? On average, as mileage increases by 1, price decreases by $0.05. On average, as mileage decreases by 0.05, price increases by $1. On average, as mileage increases by 1, price increases by $0.05, provided the year remains constant. On average, as mileage increases by 1, price decreases by $0.05, provided the year remains constant