A local real estate company wants to better understand the housing market in the Seattle area. The company hires you to do some analysis. Suppose that you have collected information on the selling price of 138 houses that have recently sold.Now suppose that in addition to the selling prices you also have data on some other characteristics of these 138 houses. The information available on each house is as follows:

PRICE = selling price (in $1,000).

SIZE = gross living space (in square feet).

AGE = age of the house (in years).

BDRM = number of bedrooms.

DOWNTOWN = 1 if the house is in downtown Seattle, 0 if outside downtown.

You run a multiple regression analysis on this data using SIZE, AGE, BDRM and DOWNTOWN to predict PRICE, which produces the following report:

Adj-R Square	0.776
Standard Error	86.772
	Coefficients	Lower 95%	Upper 95%
Intercept	180.833	-871.169	1232.834
SIZE	0.307	0.144	0.470
AGE	-3.571	-7.650	0.508
BDRM	45.421	-51.183	142.024
DOWNTOWN	175.341	28.634	322.048

One of your colleagues runs a different regression on these 138 houses, using only SIZE and DOWNTOWN as the independent variables to predict PRICE. The regressions results are as follows:

Adj-R Square	0.783
Standard Error	82.26
	Coefficients	Lower 95%	Upper 95%
Intercept	185.833	-866.9	1237.4
SIZE	0.320	0.157	0.483
DOWNTOWN	178.134	31.434	325.865

Question: Suppose you are talking to Jaime, a marketing manager in the real estate company, about this new 2-variable regression model. You tell Jaime that the confidence interval for SIZE does not contain zero, applying your knowledge from QMETH450. Jaime then asks you what your statement means. Give a precise explanation of the significance of the 95% confidence interval not containing zero.