9. Exercise 4.9 In a study of housing demand, the county assessor develops thefollowing regression model to estimate the market value (i.e.,selling price) of residential property within her jurisdiction. Theassessor suspects that important variables affecting selling price(YY , measured in thousands of dollars) are the size of ahouse (X1X1 , measured in hundreds of square feet), thetotal number of rooms (X2X2 ), age (X3X3 ), andwhether or not the house has an attached garage (X4X4 ,No=0, Yes=1No=0, Yes=1 ). Y=α+β1X1+β2X2+β3X3+β4X4+εY=α+β1X1+β2X2+β3X3+β4X4+ε Now suppose that the estimate of the model produces followingresults: a=166.048a=166.048 , b1=3.459b1=3.459 ,b2=8.015b2=8.015 , b3=−0.319b3=−0.319 ,b4=1.186b4=1.186 , sb1=1.079sb1=1.079 ,sb2=5.288sb2=5.288 ,sb3=0.789sb3=0.789 ,sb4=12.252sb4=12.252 , R2=0.838R2=0.838, F-statistic=12.919F-statistic=12.919 , andse=13.702se=13.702 . Note that the sampleconsists of 15 randomly selected observations. According to the estimated model, holding all else constant, anadditional 100 square feet of area means the market value selector1    increases decreases by approximately selector 2    $3,459 $8,015 $319 . Which of the independent variables (if any) appears to bestatistically significant (at the 0.05 level) in explaining themarket value of residential property? Check all that apply. Size of the house (X1X1 ) Total number of rooms (X2X2 ) Age (X3X3 ) Having an attached garage (X4X4 ) What proportion of the total variation in sales is explained bythe regression equation? 0.838 0.789 0.129 The given F-value shows that the assessor selector 1    cannot can reject the null hypothesis that neither of the independentvariables explains a significant (at the 0.05 level) proportion ofthe variation in income. Which of the following is an approximate 95 percent predictioninterval for the selling price of a 15-year-old house having 18hundred sq. ft., 5 rooms, and an attached garage? (237.382, 292.190) (157.232, 212.040) (170.934, 198.338)