# Question: This case involves the variables shown here and described in

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 consumersâ€™ 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.

## Answer to relevant Questions

Sam Easton started out as a real estate agent in Atlanta ten years ago. After working two years for a national real estate firm, he transferred to Dallas, Texas, and worked for another realty agency. His friends and ...In Exercise 17.12, suppose the researcher wishes to use one of the models in this section, but is uncertain as to whether y might be nonlinearly related to x1 and/or x2. Also, the researcher doesnâ€™t have any idea as to ...As x increases, y increases, but at a decreasing rate. If a second-order polynomial model were fitted to the scatterplot of the data, what would be the signs of the partial regression coefficients in the model? In a PC World comparison of computer hard drives, data describing the drives included the following variables: price (dollars), disk capacity (gigabytes), and rotational speed (RPM). The data are in file XR17027. With y = ...For the situation and variables in Exercise 17.17, fit a multiplicative model and use it to estimate the total operating revenue for an airline with 2000 employees and 10,000 departures. The underlying data are in file ...Post your question