A yard maintenance firm is trying to predict the cost of cleaning up the parking lots of shopping malls where the firm has a maintenance contract. The firm has gathered information on maintenance costs (in thousands of dollars), the number of stores in the mall (Stores), the number of square metres in the parking lot (Area), and the distance of the mall from the firms maintenance depot (Distance). A regression printout from Excel appears below.

Excel Printout

SUMMARY OUTPUT
Regression Statistics
Multiple R R Square Adjusted R Square Standard Error Observations		0.8574
		0.7352
		0.6822
		1.3276
		19
ANOVA
	df	SS	MS	F	Significance F
Regression	3	73.39	24.46	13.88	0.000133378
Residual	15	26.44	1.76
Total	18	99.83
Regression Output
Variables	Coefficients	Standard Error	t Stat	P-value
Intercept	0.8356	2.1712	0.38	0.7091
Stores	0.4145	0.1538	2.70	00166
Area	-0.0032	0.0010	-0.32	0.7562
Distance	0.1645	0.0910	1.81	0.0907

What proportion of the change in the dependent variable is explained by changes in the independent variables? Do not convert to percent or use percent sign. Keep to 4 decimal places.?

At 5% level of significance, Is the overall model valid?

		Yes, because the p-value is 0.000133378 which is less than 5%.
		No, because the p-value is 0.000133378 which is less than 5%.
		Yes, because the p-value is 0.7091 which is greater than 5%
		No, because the p-value is 0.7091 which is greater than 5%

At a 5% level of significance, which (if any) of the independent variables are significantly related to the dependent variable?

		Stores, Area and Distance
		Area and Distance
		Stores and Distance
		Stores