# Question: Seattle Home Prices This data table expands the data introduced

Seattle Home Prices This data table expands the data introduced in Chapter 19 on the prices of homes in the Seattle area. One realtor operating in Seattle listed all 28 homes for sale in the original data table. This table includes prices and sizes of 8 more homes listed by a different realtor in Seattle. As previously, we’ll look at the price per square foot, using the recip- rocal of the number of square feet as the explanatory marginal. In this model, the intercept estimates the variable cost per square foot and the slope of 1/Sq Fit estimates the fixed costs present regardless of the size of the home.

(a) Create a scatterplot of the cost per square foot of the homes on the reciprocal of the size of the homes. Do you see a difference in the relation- ship between cost per square foot and 1>Sq Fit for the two realtors? Use color-coding or different symbols to distinguish the data for the two realtors.

(b) Based on your visual impression formed in part (a), fit an appropriate regression model that describes the fixed and marginal costs for these realtors. Use a dummy variable coded as + for Realtor B to represent the different realtors in the regression.

(c) Does the estimated multiple regression fit in part

(b) Meet the conditions for the MRM?

(d) Interpret the estimated coefficients from the equation fit in part (b), if it is Okay to do so. If not, indicate why not. What does the fitted model tell you about the properties offered by the realtors?

(e) Would it be appropriate to use the estimated standard errors shown in the output of your regression estimated in part (b) to set confidence intervals for the estimated intercept and slopes? Explain.

(a) Create a scatterplot of the cost per square foot of the homes on the reciprocal of the size of the homes. Do you see a difference in the relation- ship between cost per square foot and 1>Sq Fit for the two realtors? Use color-coding or different symbols to distinguish the data for the two realtors.

(b) Based on your visual impression formed in part (a), fit an appropriate regression model that describes the fixed and marginal costs for these realtors. Use a dummy variable coded as + for Realtor B to represent the different realtors in the regression.

(c) Does the estimated multiple regression fit in part

(b) Meet the conditions for the MRM?

(d) Interpret the estimated coefficients from the equation fit in part (b), if it is Okay to do so. If not, indicate why not. What does the fitted model tell you about the properties offered by the realtors?

(e) Would it be appropriate to use the estimated standard errors shown in the output of your regression estimated in part (b) to set confidence intervals for the estimated intercept and slopes? Explain.

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