Statistics for Business & Economics (with XLSTAT Education Edition Printed Access Card) (MindTap Course List)
13th Edition
ISBN: 9781305585317
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 16.1, Problem 4E
A highway department is studying the relationship between traffic flow and speed. The following model has been hypothesized.
y = β0 + β1x + ε
where
y = traffic flow in vehicles per hour
x = vehicle speed in miles per hour
The following data were collected during rush hour for six highways leading out of the city.
| Traffic Flow (y) | Vehicle Speed (x) |
| 1256 | 35 |
| 1329 | 40 |
| 1226 | 30 |
| 1335 | 45 |
| 1349 | 50 |
| 1124 | 25 |
- a. Develop an estimated regression equation for the data.
- b. Use α = .01 to test for a significant relationship.
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Chapter 16 Solutions
Statistics for Business & Economics (with XLSTAT Education Edition Printed Access Card) (MindTap Course List)
Ch. 16.1 - Consider the following data for two variables, x...Ch. 16.1 - Consider the following data for two variables, x...Ch. 16.1 - Prob. 3ECh. 16.1 - A highway department is studying the relationship...Ch. 16.1 - In working further with the problem of exercise 4,...Ch. 16.1 - A study of emergency service facilities...Ch. 16.1 - In 2011, home prices and mortgage rates fell so...Ch. 16.1 - Corvette, Ferrari, and Jaguar produced a variety...Ch. 16.1 - Kiplingers Personal Finance Magazine rated 359...Ch. 16.2 - In a regression analysis involving 27...
Ch. 16.2 - In a regression analysis involving 30...Ch. 16.2 - The Ladies Professional Golfers Association (LPGA)...Ch. 16.2 - Refer to exercise 12. a. Develop an estimated...Ch. 16.2 - A 10-year study conducted by the American Heart...Ch. 16.2 - In baseball, an earned run is any run that the...Ch. 16.4 - A study provided data on variables that may be...Ch. 16.4 - Prob. 17ECh. 16.4 - Jeff Sagarin has been providing sports ratings for...Ch. 16.4 - Prob. 19ECh. 16.5 - Consider a completely randomized design involving...Ch. 16.5 - Prob. 21ECh. 16.5 - Prob. 22ECh. 16.5 - The Jacobs Chemical Company wants to estimate the...Ch. 16.5 - Four different paints are advertised as having the...Ch. 16.5 - An automobile dealer conducted a test to determine...Ch. 16.5 - A mail-order catalog firm designed a factorial...Ch. 16.6 - The following data show the daily closing prices...Ch. 16.6 - Refer to the Cravens data set in Table 16.5. In...Ch. 16 - A sample containing years to maturity and yield...Ch. 16 - Consumer Reports tested 19 different brands and...Ch. 16 - A study investigated the relationship between...Ch. 16 - Refer to the data in exercise 31. Consider a model...Ch. 16 - Refer to the data in exercise 31. a. Develop an...Ch. 16 - Prob. 34SECh. 16 - Rating Wines from the Piedmont Region of Italy...
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardLife Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forward
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