The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts. Based on this output and your understanding of multiple regression analysis, what percentage of variation in the dependent variable is explained by the regression model? Click the icon to view the summary output table. - X A. about 37 percent Summary output table O B. over 90 percent O C. approximately 82 percent O D. none of the above Regression Statistics Multiple R 0.906876 R Square Adjusted R Square Standard Error Observations 30 ANOVA SS MS F Regression Residual 89.88341785 Total 506.1666667 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 41.77425 5.612843566 Curb Weight -0.00608 0.00145286 cylinders 0.959134 0.887531206 Horse Power 0.052399 0.023938405 Torque -0.01747 0.024737052 0 to 60 mph 0.492998 0.486960357 Price as Tested -0.00015 4.31238E-05 Displacement -2.11223 0.88675014