# Question

1. What’s the estimated value from the ANOVA regression given by the slopes in Table 4 for the change in sales in the Midwest if ads feature the small labor partition? What’s the simple way to get this estimate?

2. The standard errors of the slopes of the dummy variables in Table 4 are all the same (80.57) and less than the common standard error of the interactions (113.94). Why is that?

3. What happens to the profile plot in Figure 1 if we reverse the rows and columns of the data? (Let Price Partition define the x-axis and connect points in the same region.) Sketch the new plot. Do you still see the presence of interaction?

4. We don’t actually need further calculations to compare the effects of the price partitions within regions; everything we need is in Table 1 and Table 2. Using those tables, identify a statistically significant difference among the means in the Northeast. Be sure to adjust for multiple comparisons (Chapter 26).

5. The analysis shown here uses a balanced experiment, with equal numbers of observations for each pairing of the factors. What happens to the estimates and their standard errors if that’s not the case? Does your software still work? Try it! Delete a few rows of the data and see what happens.

6. The structure of these data resembles the structure of the data in the pricing example, but there are differences.

(a) If we consider the satisfaction data as an experiment, with response Rating, are the data balanced?

(b) What are the implications for the later analysis of your answer to (a)?

7. (a) What do you learn from a one-way analysis of variance of Rating by Vehicle Category?

(b) What do you learn from a one-way analysis of variance of Rating by Manufacturer?

8. Perform a two-way analysis of variance of Rating by Vehicle Type and Manufacturer. Use economy cars from the United States as the baseline group.

(a) Summarize your model.

(b) Show a profile plot of the estimated model. Does the profile plot suggest the presence of an interaction?

(c) Are these data compatible with the assumptions of an ANOVA regression?

(d) If appropriate, which effects are statistically significant?

9. Show that the estimated rating from the model for premium cars from Asia matches the average rating of cars in this group.

10. Interpret the results of this model. How does the two-way analysis change your interpretation of the results found in Question 7?

A consumer products company surveys people who recently bought new cars. Each customer is asked to express satisfaction with the purchased car by stating the likelihood (from 0 to 10) of buying another of the same brand in the future. A rating of 0 means no chance of buying an-other, and 10 means the likelihood is almost certain. The data in this example give the ratings of 220 purchasers of economy, standard, and premium vehicles from Asian, European, and domestic manufacturers.

2. The standard errors of the slopes of the dummy variables in Table 4 are all the same (80.57) and less than the common standard error of the interactions (113.94). Why is that?

3. What happens to the profile plot in Figure 1 if we reverse the rows and columns of the data? (Let Price Partition define the x-axis and connect points in the same region.) Sketch the new plot. Do you still see the presence of interaction?

4. We don’t actually need further calculations to compare the effects of the price partitions within regions; everything we need is in Table 1 and Table 2. Using those tables, identify a statistically significant difference among the means in the Northeast. Be sure to adjust for multiple comparisons (Chapter 26).

5. The analysis shown here uses a balanced experiment, with equal numbers of observations for each pairing of the factors. What happens to the estimates and their standard errors if that’s not the case? Does your software still work? Try it! Delete a few rows of the data and see what happens.

6. The structure of these data resembles the structure of the data in the pricing example, but there are differences.

(a) If we consider the satisfaction data as an experiment, with response Rating, are the data balanced?

(b) What are the implications for the later analysis of your answer to (a)?

7. (a) What do you learn from a one-way analysis of variance of Rating by Vehicle Category?

(b) What do you learn from a one-way analysis of variance of Rating by Manufacturer?

8. Perform a two-way analysis of variance of Rating by Vehicle Type and Manufacturer. Use economy cars from the United States as the baseline group.

(a) Summarize your model.

(b) Show a profile plot of the estimated model. Does the profile plot suggest the presence of an interaction?

(c) Are these data compatible with the assumptions of an ANOVA regression?

(d) If appropriate, which effects are statistically significant?

9. Show that the estimated rating from the model for premium cars from Asia matches the average rating of cars in this group.

10. Interpret the results of this model. How does the two-way analysis change your interpretation of the results found in Question 7?

A consumer products company surveys people who recently bought new cars. Each customer is asked to express satisfaction with the purchased car by stating the likelihood (from 0 to 10) of buying another of the same brand in the future. A rating of 0 means no chance of buying an-other, and 10 means the likelihood is almost certain. The data in this example give the ratings of 220 purchasers of economy, standard, and premium vehicles from Asian, European, and domestic manufacturers.

## Answer to relevant Questions

The debate about the future of Social Security has renewed interest in the amount saved for retirement by employees at a company. While it is possible to open an Individual Retirement Account (IRA), many employees may not ...1. The average cost for rushed jobs is less than the average cost for those not rushed ($40.43 versus $38.59). Usually, having to do things in a hurry increases costs. Add the dummy variable D(Rush) to the stepwise model. ...Smoothing reduces the random variation in data, producing a sequence that reveals the systematic trend in the data. Shouldn’t we build models from the smoothed data, which have less random noise, rather than from the ...This analysis compares the model in the text that has the level of shipments as the response to a model that uses the change in the shipments as the response. (These data are monthly, based on the seasonally adjusted data ...Bookstore Physical stores face increasing competition from online rivals. To compete, a campus bookstore collected data on sales of newly released trade books displayed on shelving near the store entrance. These data give ...Post your question

0