Bundle: Statistics for Business & Economics, Loose-Leaf Version, 13th + MindTap Business Statistics with XLSTAT, 1 term (6 months) Printed Access Card
13th Edition
ISBN: 9781337148092
Author: David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran
Publisher: Cengage Learning
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Textbook Question
Chapter 14.6, Problem 39E
In exercise 12, the following data on x = average daily hotel room rate and y = amount spent on entertainment (The Wall street Journal, August 18, 2011) lead to the estimated regression equation ŷ = 17.49 + 1.0334x. For these data SSE = 1541.4.
| City | Room Rate ($) |
Entertainment ($) |
| Boston | 148 | 161 |
| Denver | 96 | 105 |
| Nashville | 91 | 101 |
| New Orleans | 110 | 142 |
| Phoenix | 90 | 100 |
| San Diego | 102 | 120 |
| San Francisco | 136 | 167 |
| San Jose | 90 | 140 |
| Tampa | 82 | 98 |
- a. Predict the amount spent on entertainment for a particular city that has a daily room rate of $89.
- b. Develop a 95% confidence interval for the
mean amount spent on entertainment for all cities that have a daily room rate of $89. - c. The average room rate in Chicago is $128. Develop a 95% prediction interval for the amount spent on entertainment in Chicago.
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Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the
predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result
compare to the actual height of 1776 mm?
Foot Length 281.9 278.3 252.9 258.7 279.2 258.0 274.2 262.3
Height
1785.0 1771.0 1675.9 1646.2 1858.8 1709.6 1788.7 1736.6
The regression equation is ŷ= + (x
y=
(Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.)
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Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm?
Foot Length 282.0 278.0 252.7 259.0 278.9 257.8 274.1 262.3
Height
1785.0 1770.9 1676.3 1646.0 1859.3 1710.1 1789.3 1737.2
The regression equation is ŷ = + (x.
(Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.)
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The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below.
What proportion of variation in the number of complaints can be explained by hourly wages?
From the results shown above, write the regression equation
If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?
Chapter 14 Solutions
Bundle: Statistics for Business & Economics, Loose-Leaf Version, 13th + MindTap Business Statistics with XLSTAT, 1 term (6 months) Printed Access Card
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- Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.1 253.3 259.4 279.1 257.8 273.6 262.2 Height 1785.0 1771.2 1676.2 1646.2 1858.9 1710.2 1788.7 1736.6 The regression equation is y=+x. X. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? OA. The result is very different from the actual height of 1776 mm. OB. The result is exactly the same as the actual height of 1776 mm. OC. The result is close to the actual height of 1776 mm. OD. The result does not make sense given the context of the data.arrow_forwardThe U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive? Is the regression coefficient statistically significant? How can you tell?arrow_forwardA FIFA World Cup football is dropped from 35 different heights (in cm) and the height of the bounce is recorded (in cm.) The regression analysis gives the model bounce = -0.1 +0.70 drop. Predict the height of the bounce if dropped from 64 cm. 44.7 cm 44.8 cm 64.6 cm 91.57 cm 44.9 cmarrow_forward
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