A developer who specializes in summer cottage properties is considering purchasing a large tract of land adjoining a lake. The current owner of the tract has already subdivided the land into separate building lots and has prepared the lots by removing some of the trees. The developer wants to forecast the value of each lot. From previous experience, the developer knows that the most important factors affecting the price (in $1000s) of the lot are size (in thousands sq .ft.), number of mature trees, and distance (in feet) to the lake. From a nearby area, the developer gathers the relevant data for 60 recently sold lots. A multiple linear regression was performed using Excel.

SUMMARY

Regression Statistics
Multiple R	0.4924
R Square	0.2425
Adjusted R Square	0.2019
Standard Error	40.2435
Observations	60
ANOVA
	df	SS	MS	F	Sig F
Regression	3	29029.72	9676.572	5.9749	0.0013
Residual	56	90694.33	1619.542
Total	59	119724
	Coefficients	Standard Error	t Stat	P-value
Intercept	51.3912	23.5165	2.1853	0.0331
Lot size	0.6999	0.5589	1.2524	0.2156
Trees	0.6788	0.2293	2.9603	0.0045
Distance	-0.3784	0.1952	-1.9380	0.0577

The regression equation is?

Based on the EXCEL printout, is the variable lot size significantly related to sale price at 5% level of significance?

Based on the EXCEL printout, is the variable number of trees significantly related to sale price at 5% level of significance?

What is the proportion of variation in sale price accounted for by the set of independent variables? Express in four decimal places. For example, 50% is 0.5000. Use 4 decimal places.?