Question 18 A multiple regression is shown for a data set of yachts where the dependent variable is the price in thousands of dollars. Regression Statistics Multiple R 0.82731 R Square 0.68445 Adjusted R Square 0.60030 Standard Error 37.40252 Observations 20 ANOVA dy SS MS F Significance F Regression 4 45515.77 11378.94 8.13 0.00107 Residual 15 20984.23 1398.95 Total 19 66500.00 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 120.50886 82.22254 1.46564 0.16339 -54.74434 295.76205 Rooms 7.46415 12.24022 0.60981 0.55112 -18.62526 33.55355 Age -1.7783 0.7179 -2.47713 0.02564 -0.33084 -0.02482 Length 2.82719 1.40160 2.01711 0.06195 -0.16025 5.81463 Nav. Equip. 0.35408 0.22411 1.57995 0.13497 -0.12360 0.83176 Based on this output, which of the independent variables appear to be significantly helping to predict the price of a yacht, using a 0.10 level of significance? Age and length Length and Nav. Equip. Age Rooms and Nav. Equip