Items 1 thru 4 are based on the following information:
Lackland Ski Resort uses multiple regression to predict ski lift revenue for the next week based on the forecasted number of dates with temperatures above 10 degrees and predicted number of inches of snow. The following function has been developed:
Sales = 10,902 + (255 × no. of days predicted above 10 degrees) + (300 × no. of inches of snow predicted)
Other information generated from the analysis include
|
Coefficient of determination (Adjusted r squared) |
.6789 |
|
Standard error |
1,879 |
|
F-Statistic |
6.279 with a significance of .049 |
Which variables(s) in this function is(are) the dependent variable(s)?
- Predicted number of days above 10 degrees.
- Predicted number of inches of snow.
- Revenue.
- Predicted number of days above 10 degrees and predicted number of inches of snow.
Assume that management predicts the number of days above 10 degrees for the next week to be 6 and the number of inches of snow to be 12. Calculate the predicted amount of revenue for the next week.
- $10,902
- $11,362
- $16,032
- $20,547
Which of the following represents an accurate interpretation of the results of Lackland’s regression analysis?
- 6.279% of the variation in revenue is explained by the predicted number of days above 10 degrees and the number of inches of snow.
- The relationships are not significant.
- Predicted number of days above 10 degrees is a more significant variable than number of inches of snow.
- 67.89% of the variation in revenue is explained by the predicted number of days above 10 degrees and the number of inches of snow.
Assume that Lackland’s model predicts revenue for a week to be $13,400. Calculate the 95% confidence interval for the amount of revenue for the week. (The 95% confidence interval corresponds to the area representing 2.3436 deviations from the mean.)
- $13,400 ± 6,279
- $13,400 ± 4,404
- $13,400 ± 6,786
- $13,400 ± 8,564