1. 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	0.99837267
R Square	0.99674799
Adjusted R Square	0.99457999
Standard Error	970.578463
Observations	11
ANOVA
	Df	SS	MS	F
Regression	4	1732393319	4.33E+08	459.7536742
Residual	6	5652135.316	942022.6
Total	10	1738045455
	Coefficients	Standard Error	t Stat	P-value
Intercept	52317.8305	12237.3616	4.275254	0.005232793764
Floor Space (x1)	27.6413874	5.429374042	5.091082	0.002240962381
Offices (x2)	12529.7682	400.0668382	31.31919	0.000000070386
Entrances (x3)	2553.21066	530.6691519	4.811304	0.002966281803
Age (x4)	-234.23716	13.26801148	-17.6543	0.000002120610

1. Write down the multiple regression equation. (2 Marks)
2. How much is the % variation in the value of an office building that is explained by the independent variables. (1 Marks)
3. Interpret the regression coefficients X3. (1 Marks)
4. 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 Marks)