To study the factors that affect monthly premium paid for car insurance, a random sample of 138 customers who recently bought car insurance is taken. Researchers ran a regression of the premium paid by customers (in dollars) on the ages of their cars (in years), number of years since they obtained their driver's licenses (in years) and how long they usually use their cars each day (in hours). SUMMARY OUTPUT Regression Statistics Multiple R 0.917475554 R Square 0.841761392 Adjusted R Square 0.838218737 Standard Error 55.80235933 Observations 138 ANOVA df SS MS F Significance F Regression 3 2219660.071 739886.6903 237.6074712 1.9277E-53 Residual 134 417263.0431 3113.903307 Total 137 2636923.114 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 8 0: Intercept 219.5397885 13.37425332 16.41510619 7.1016E-34 193.0878458 245.9917312 6 1 : Age of car 26.600448 1.416523719 18.77868167 2.39594E-39 23.7988109 29.4020851 8 2: Years after licence -29. 69754656 1.721192453 -17.25405343 7.50789E-36 -33.10176533 -26.29332779 8 3 : Hours of daily use 3.621672171 1.901888819 1.904250204 0.059021235 -0.139932524 7.383276866 What does the ANOVA in the middle table test? O It tests Ho: B 1 = B2 VS. Ha: B1 * B2 It tests Ho: B1 = B2 = B3 =0 vs. Hai at least one Bi (i = 1, 2 or 3) is not equal to zero. O It tests Ho: Bo = O vs. Ha: Bo =0 O It tests Ho: var(B,) = var(B2) vs. Ha: var(B,) = var(B2)