A local real estate company wants to better understand the housing market in the Bethesda/Rockville area. The company hires you to do some analysis. Suppose that you have collected information on the selling price of 218 houses that are randomly selected from recent transactions. You compute the sample mean of these prices to be Y-bar = $1,006,670 and the sample standard deviation to be s = $172,400. The distribution of the selling prices are given below.

Now suppose that in addition to the selling prices you also have data on some other characteristics of these 218 houses. The information available on each house is as follows:

PRICE = selling price

SIZE = gross living space (in square feet)

AGE = age of the house (in years)

BEDROOM = number of bedrooms

TOWN = 0 if the house is in Bethesda (Red), 1 if in Rockville (Green)

You run a multiple regression analysis on this data using SIZE, BEDROOM, AGE, and TOWN to predict PRICE, which produces the following report. Unfortunately, you accidentally spill coffee on the report and this leaves two cells blacked-out:

Summary of Fit

RSquare
RSquare Adj	0.91498
Root Mean Square Error(Standard Error)	50268.32
Mean of Response	1006697
Observations (or Sum Wgts)	218

Analysis of Variance

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	4	5.91132e12	1.4778e12	584.8378
Error	213	5.3823e+11	2.5269e+9	Significance F
C. Total	217	6.44955e12		<.0001

Parameter Estimates

Term		Estimate	Std Error	t Ratio	p-value|
Intercept		244984.16	21937.82	11.16	<.0001
Size		388.36313	12.22978	31.76	<.0001
Bedroom		7471.2622	6883.59	1.09
Age		-1966.488	1581.758	-1.24	0.2152
Town		-98941.04	3505.532	-28.22	<.0001

Residual by Predicted Plot

For RSquare:

		0.9165
		0.9278
		0.9021
		0.9349