# Question

This case involves the variables shown here and described in data file SHOPPING. Information like that gained from this case could provide management with useful insights into how two or more variables (including dummy variables) can help describe consumers’ overall attitude toward a shopping area.

1. With variable 7 (attitude toward Springdale Mall) as the dependent variable, perform a multiple regression analysis using variables 21 (good var iety of sizes/ styles), 22 (sales staff helpful/friendly), 26 (gender), and 28 (marital status) as the four indepen dent variables. If possible, have the residuals and the predicted y values retained for later analysis.

a. Interpret the partial regression coefficient for variable 26 (gender). At the 0.05 level, is it significantly different from zero? If so, what does this say about the respective attitudes of males versus females toward Springdale Mall? Interpret the other partial regression coefficients and the results of the significance test for each.

b. At the 0.05 level, is the overall regression equation significant? At exactly what p-value is the equation significant?

c. What percentage of the variation in y is explained by the regression equation? Explain this percentage in terms of the analysis of variance table that accompanies the printout.

d. If possible with your computer package, generate a plot of the residuals (vertical axis) versus each of the independent variables (horizontal axis).

Evaluate each plot in terms of whether patterns exist that could weaken the validity of the regression model.

e. If possible with your computer statistical package, use the normal probability plot to examine the residuals.

2. Repeat question 1, but with variable 8 (attitude toward Downtown) as the dependent variable.

3. Repeat question 1, but with variable 9 (attitude toward West Mall) as the dependent variable.

4. Compare the regression equations obtained in questions 1, 2, and 3. For which one of the shopping areas does this set of independent variables seem to do the best job of predicting shopper attitude? Explain your reasoning.

1. With variable 7 (attitude toward Springdale Mall) as the dependent variable, perform a multiple regression analysis using variables 21 (good var iety of sizes/ styles), 22 (sales staff helpful/friendly), 26 (gender), and 28 (marital status) as the four indepen dent variables. If possible, have the residuals and the predicted y values retained for later analysis.

a. Interpret the partial regression coefficient for variable 26 (gender). At the 0.05 level, is it significantly different from zero? If so, what does this say about the respective attitudes of males versus females toward Springdale Mall? Interpret the other partial regression coefficients and the results of the significance test for each.

b. At the 0.05 level, is the overall regression equation significant? At exactly what p-value is the equation significant?

c. What percentage of the variation in y is explained by the regression equation? Explain this percentage in terms of the analysis of variance table that accompanies the printout.

d. If possible with your computer package, generate a plot of the residuals (vertical axis) versus each of the independent variables (horizontal axis).

Evaluate each plot in terms of whether patterns exist that could weaken the validity of the regression model.

e. If possible with your computer statistical package, use the normal probability plot to examine the residuals.

2. Repeat question 1, but with variable 8 (attitude toward Downtown) as the dependent variable.

3. Repeat question 1, but with variable 9 (attitude toward West Mall) as the dependent variable.

4. Compare the regression equations obtained in questions 1, 2, and 3. For which one of the shopping areas does this set of independent variables seem to do the best job of predicting shopper attitude? Explain your reasoning.

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