Refer to the residential sales data in Problem 19 in Chapter 12. Use the computer output given below to answer the following questions.
a. State an ANACOVA regression model that can be used to compare outer suburbs with other locations (intown and inner suburbs), controlling for age.
b. Identify the model that should be used to check whether the ANACOVA model in part (a) is appropriate. Carry out the appropriate test.
c. Using ANACOVA, determine adjusted mean sales prices for the two locations, and test whether they differ significantly from one another. (Mean house age = 10.8667; unadjusted mean sales price (in $l,000s) for intown and inner suburbs = 82.1867; unadjusted mean sales price (in $ 1,000s) for outer location = 84.8067.)
Regression of Sales Price (Y) on Age (X4), Location (Z), and X4 × Z

Regression of Sales Price (Y) on Age (X4), Location (Z), and X4 × Z
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ANALYSIS OF VARIANCE Sum of DF Squares Mean Square 548.71456 73.37946 F Value Pr> F Source Model Error Corrected Total 748 0.0009 3 26 29 1646.14368 1907.86599 3554.00967 Root MSE Dependent Mean Coeff Var 8.56618 83.49667 1025931 R-Square 0.4632 Adj R-Sq 0.4012 PARAMETER ESTIMATES Parameter Standard Variable Intercept X4 DF Estimate Error t Value 6.85947 PrtType I SS 55.45706 2.37245 057631 209151 4.12 0.0003 1324.84735 114.99710 0.1056 206.29922 8.08 <.0001 17.90588 8.90573 2.01 0.0548 X42 -1.27910 0.76286 -1.68 ANALYSIS OF VARIANCE Sum of DFI Squares Mean Square Source Model Error Corrected Total F Value Pr>F 9.19 0.0009 2 143984445 719.92223 78.30242 27 2114.16521 29 3554.00967 Root MSE Dependent Mean Coeff Var 8.84887 83.49667 10.59787 0.4051 0.3611 R-Square Adj R-Sq PARAMETER ESTIMATES Parameter Standard Error tValue Pr t 4.95305 DF Estimate Variable Intercept x4 63.68190 12.86 000 1.64244 0.39005 3.93395 324618 1.21 4.21 0.0003 02361