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15. For Suppose a commercial developer is considering purchasing a group of small office buildings in an established business district. The developer can use multiple linear regression analysis to estimate the value of an office building in each area based on the following variables. y = Assessed value of the office building x1 = Floor space in square feet x2 = Number of offices x3= Number of entrances x4 = Age of the office building in years SUMMARY OUTPUT Regression Statistics Multiple R 10.99837267 R Square 0.99674799 Adjusted R Square 0.99457999 Standard Error 970.578463 Observations 11 ANOVA DI F 459.7536742 Regression Residual Total SS MS 4 17323933194.33E+08 65652135,316 942022.6 17380454551 10 Standard Cocfficiens Error Star P-value Intercept 52317.8305 12237.36164.2752540.005232793764 Floor Space (xl) 27.6413874 5.429374042 5.091082 0.002240962381 Offices (x2) 12529.7682 400.0668382 31.319190.000000070386 Entrances (x3) 2553.21066 530.6691519 4.811304 0.002966281803 Age (x4) -234.23716 13.26801148 - 17.65430.000002120610 (a), Write down the multiple regression equation. (2 Marks) (b). How much is the % variation in the value of an office building that is explained by the independent variables. (1 Marks) (c). Interpret the regression coefficients X3. (1 Marks) (d). At the 5% level of significance, which of the 4 independent variables is NOT statistically significant in the determination of the value of an office building? Explain your answer. (1 Mirh