In a study of housing demand, the county assessor develops the following regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. The assessor suspects that important variables affecting selling price (Y, measured in thousands of dollars) are the size of a house (X, measured in hundreds of square feet), the total number of rooms (X2), age (X3), and whether or not the house has an attached garage (X4,No=0,Yes=1).

Y=+1X1+2X2+3X3+4X4+

Now suppose that the estimate of the model produces following results:a=185.265,b

1=3.921,b2=3.585b3=0.118,b4=2.832,sb1=0.755,sb2=4.471,sb3=0.641,sb4=9.717,R2

=0.893,F-statistic=20.848, andse=11.135. Note that the sample consists of 15 randomly selected observations.

According to the estimated model, holding all else constant, an additional year in age means the market value__________ (DECREASES OR INCREASES)by approximately______________ ($2,832 or $3,585 or $118).

Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining the market value of residential property?Check all that apply.

A. Size of the house (X1)

B. Total number of rooms (X2)

C. Age (X3)

D. Having an attached garage (X4)

What proportion of the total variation in sales is explained by the regression equation?

A. 0.208

B. 0.641

C. 0.893

The given F-value shows that the assessor____________ (CAN OR CANNOT)reject the null hypothesis that neither of the independent variables explains a significant (at the 0.05 level) proportion of the variation in income.

Which of the following is an approximate 95 percent prediction interval for the selling price of a 15-year-old house having 19.00 hundred sq. ft., 3 rooms, and an attached garage?

A. (243.647, 288.187)

B. (222.137, 266.677)

C. (233.272, 255.542)