Zip code-level data on prices for various items at fast-food restaurants, along with characteristics of the...

 

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Zip code-level data on prices for various items at fast-food restaurants, along with characteristics of the zip code population, in New Jersey and Pennsylvania was collected. The idea is to see whether fast-food restaurants charge higher prices in areas with a larger concentration of blacks. First, consider a model to explain the price of soda, psoda, in terms of the proportion of the population that is black (prpblck) and median income of a zip-code income. Second consider a model to explain the price of soda, psoda, in terms of the proportion of the population that is black (prpblck) only. The following table provides the regression results of the two model. . regress psoda prpblck income Source Model Residual Total psoda prpblck income cons Source Model Residual Total SS regress psoda prpblck psoda prpblck cons Coef. .202552215 2 2.95146493 398 3.15401715 400 007885043 .1149882 1.60e-06 .9563196 SS df Coef. 0649269 1.037399 .101276107 .007415741 Std. Err. .0260006 3.62e-07 .018992 df MS t 4.42 4.43 50.35 MS .057010466 1 .057010466 3.09700668 399 .007761922 3.15401715 400 Std. Err. .007885043 P>|t| t P>|t| . 023957 2.71 0.007 .0051905 199.87 0.000 0.000 0.000 0.000 Number of obs = F( 2, 398) Prob > F R-squared Adj R-squared Root MSE [95% Conf. Interval] .0638724 8.91e-07 .9189824 Number of obs = 399) F( 1, Prob > F R-squared 401 13.66 0.0000 0.0642 0.0595 .08611 Adj R-squared = Root MSE = .1661039 2.31e-06 .9936568 .0178292 1.027195 401 7.34 0.0070 0.0181 0.0156 .0881 [95% Conf. Interval] . 1120245 1.047603 4a) What can you say about the two results? Do you think it is economically large? What does the R² tell you? Zip code-level data on prices for various items at fast-food restaurants, along with characteristics of the zip code population, in New Jersey and Pennsylvania was collected. The idea is to see whether fast-food restaurants charge higher prices in areas with a larger concentration of blacks. First, consider a model to explain the price of soda, psoda, in terms of the proportion of the population that is black (prpblck) and median income of a zip-code income. Second consider a model to explain the price of soda, psoda, in terms of the proportion of the population that is black (prpblck) only. The following table provides the regression results of the two model. . regress psoda prpblck income Source Model Residual Total psoda prpblck income cons Source Model Residual Total SS regress psoda prpblck psoda prpblck cons Coef. .202552215 2 2.95146493 398 3.15401715 400 007885043 .1149882 1.60e-06 .9563196 SS df Coef. 0649269 1.037399 .101276107 .007415741 Std. Err. .0260006 3.62e-07 .018992 df MS t 4.42 4.43 50.35 MS .057010466 1 .057010466 3.09700668 399 .007761922 3.15401715 400 Std. Err. .007885043 P>|t| t P>|t| . 023957 2.71 0.007 .0051905 199.87 0.000 0.000 0.000 0.000 Number of obs = F( 2, 398) Prob > F R-squared Adj R-squared Root MSE [95% Conf. Interval] .0638724 8.91e-07 .9189824 Number of obs = 399) F( 1, Prob > F R-squared 401 13.66 0.0000 0.0642 0.0595 .08611 Adj R-squared = Root MSE = .1661039 2.31e-06 .9936568 .0178292 1.027195 401 7.34 0.0070 0.0181 0.0156 .0881 [95% Conf. Interval] . 1120245 1.047603 4a) What can you say about the two results? Do you think it is economically large? What does the R² tell you?

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