regression

Consider the following data for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house, the size of the lot and the size of the house in square feet. Summary Output Regression Statistics Multiple R 0.8 R Square 0.67 Adjusted R Square 0.66 Standard Error 59.83 Observations 88 ANOVA SS MS Significance F Regression 3 617130.7 205710 57.46 2:6696 Residual 84 300723 3580.05 Total 87 917854 5 Standard Coefficents error at test P.value Lower 9595 Upper 9595 Intercept -21.77 29.48 -0.7386 . 0.46 80.38 36.844 Bedrooms 13.85 9.01 0.13 -4.065 31.77 Lot size 0.002 0.00054 3.22 0.002 0.00079 0:003344 Square feet 0.1228 0.013 9.275 1.65E-14 0.09645 0.149 Which independent variable is most significant in this regression relationship? The number of bedrooms O The size of the lot O The size of the house in square feet because the p-value is 1.65E-14 O The size of the house in square feet because the coefficient is 0.1228