1. 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

Predict the selling price of 40,000 sq ft. lot (use 40), 50 matures trees, and 25 feet from the lake. Express your answer in dollars with 2 decimal places but without the dollar sign. For example, $123,456.90 is 123457.90?

Given the correlation matrix below, what can you conclude?

Is the variance of the error variable constant?