Cars.com would like to use simple regression to predict the selling price of a used car, in thousands of dollars, based on the age of the car in years. A random sample of used cars was selected and the result of the regression analysis is shown below. SUMMARY OUTPUT Regression Statistics Multiple R 0.7885 R Square 0.6217 Adjusted R Square 0.5461 Standard Error 1.4528 Observations 7 ANOVA df F Significance F 8.2182 0.0351 Regression Residual Total SS MS 17.3464 17.3464 10.5536 2.1107 27.9 5 Intercept Age Coefficients Standard Error t Stat P-value 11.6881 1.2403 9.4237 0.0002 -0.5072 0.1769 -2.8667 0.0351 Lower 95% Upper 95% 8.4999 14.8764 -0.9620 -0.0524 Which one of the following statements is true using a =0.05? Because the p-value for the t statistic of -2.8667 is 0.0351, smaller than the significance level of a=0.050 , we reject the null hypothesis and conclude that there is significant evidence from the sample data that the age of a used car does influence its selling price. ( Because the p-value for the t statistic of -2.8667 is 0.0002, smaller than the significance level of a=0.050 , we can reject the null hypothesis and conclude that there is evidence from the sample data that the age does influence the selling price of a used car. Because the p-value for the t statistic of -2.8667 is 0.0351, smaller than the significance level of a=0.050 , we can reject the null hypothesis and conclude that there is no relationship between the age and selling price of a used car. D Because the p-value for the t statistic of 1.2403 is 0.0002, smaller than the significance level of a=0.050 , we fail to reject the null hypothesis and we can not conclude that there is a relationship between the age and selling price of a used car