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: 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 0.88675014Based on this output and your understanding of multiple regression analysis, what is the critical value for testing the significance of the overall regression model at a 0.05 level of statistical significance? 6) Approximately F = 2.50 0 About F = 5.92 0 Nearly F = 3.30 O None of the above