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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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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Nine pairs of data yield a regression equation of y=0.93x + 19.4, with r= 0.967 and an average y value of 64.70. What is best predicted value for y when x =65?
Is it 79.85, 25.74, 89.61, 57.82, 70.55, 96.70, 19.40, or 64.70?
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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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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