2. Suppose we want to investigate whether there is a systematic difference in sale prices between homes that are vacant during the time they are on the market with those that are not vacant. Sale prices are modelled using in a "hedonic" regression model and applied to a data set that consists of 880 home sales in Stockton, CA during mid- 2005. The data in the files include the following variables: Variable Name price soft sgftsg beds baths Age stories Vacant Description house price, $ (in thousands) total square feet of living area Square root of the housing size variable. number of bedrooms number of full baths age, in years number of stories Yes = 1 No = 0 A Vacant house is empty, meaning that there is no furniture or decorations in house. The results of this analysis are presented below. The REG Procedure Model: MODEL 1 Dependent Variable: price price 880 Number of Observations Read Number of Observations Used 880 Analysis of Variance DF Mean Square F Value Pr>F Sum of Source Squares Model 6 1.822898E12 873 6.289028E11 Corrected Total 879 2.451801E12 421.74 <.0001 error root mse dependent mean coeff var r-square adj r-sa parameter estimates variable label estimate standard df t value pr> Intercept Intercept 1 91150 7888.41938 11.55 <.0001 age baths beds sqfth sqfthsa stories the reg procedure model: model dependent variable: price vacant="0" number of observations read used analysis variance source df sum squares mean square f value pr>F Model 6 1.14392E12 1.906533E11 206.37 <.0001 error corrected total root mse dependent mean r-square adj r-sq coeff var parameter estimates variable label df estimate standard t value pr> It Intercept Intercept 1 95022 13417 7.08 <.0001 age baths beds sqfth sqfths stories the reg procedure model: model dependent variable: price vacant="1" number of observations read used analysis variance source df sum squares mean square f value pr>F 230.89 <.0001 model error corrected total root mse r-square adj r-sq coeff var parameter estimates variable label intercept age standard df estimate t value pr> 10.49 <.0001 baths beds sqfth sqfths stories a. home staging where you decorate a vacant house prior to putting it on the market is popular among relators. why might expect that relators would recommend this points b. construct relevant test data for and non-vacant housing can be pooled interpret results. c. comment any important differences in value of specific characteristics seem vary between homes support realtor argument good idea before put market. suppose we want investigate whether there systematic difference sale prices are during time they with those not vacant. modelled using regression model applied set consists sales stockton ca mid- files include following variables: variable name price soft sgftsg age description thousands total square feet living area root size variable. number bedrooms full years yes="1" no="0" empty meaning furniture or decorations house. results analysis presented below. reg procedure model: dependent variable: observations read used variance df mean f pr>F Sum of Source Squares Model 6 1.822898E12 873 6.289028E11 Corrected Total 879 2.451801E12 421.74 <.0001 error root mse dependent mean coeff var r-square adj r-sa parameter estimates variable label estimate standard df t value pr> Intercept Intercept 1 91150 7888.41938 11.55 <.0001 age baths beds sqfth sqfthsa stories the reg procedure model: model dependent variable: price vacant="0" number of observations read used analysis variance source df sum squares mean square f value pr>F Model 6 1.14392E12 1.906533E11 206.37 <.0001 error corrected total root mse dependent mean r-square adj r-sq coeff var parameter estimates variable label df estimate standard t value pr> It Intercept Intercept 1 95022 13417 7.08 <.0001 age baths beds sqfth sqfths stories the reg procedure model: model dependent variable: price vacant="1" number of observations read used analysis variance source df sum squares mean square f value pr>F 230.89 <.0001 model error corrected total root mse r-square adj r-sq coeff var parameter estimates variable label intercept age standard df estimate t value pr> 10.49 <.0001 baths beds sqfth sqfths stories a. home staging where you decorate a vacant house prior to putting it on the market is popular among relators. why might expect that relators would recommend this points b. construct relevant test data for and non-vacant housing can be pooled interpret results. c. comment any important differences in value of specific characteristics seem vary between homes support realtor argument good idea before put market.>
