Modern Business Statistics with Microsoft Office Excel (with XLSTAT Education Edition Printed Access Card) (MindTap Course List)
6th Edition
ISBN: 9781337115186
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, Problem 2CP
As part of a study on transportation safety, the U.S. Department of Transportation collected data on the number of fatal accidents per 1000 licenses and the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Data collected over a one-year period follow. These data are contained in the file named Safety.
- 1. Develop numerical and graphical summaries of the data.
- 2. Use
regression analysis to investigate the relationship between the number of fatal accidents and the percentage of drivers under the age of 21. Discuss your findings. - 3. What conclusion and recommendations can you derive from your analysis?
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Chapter 14 Solutions
Modern Business Statistics with Microsoft Office Excel (with XLSTAT Education Edition Printed Access Card) (MindTap Course List)
Ch. 14.2 - Given are five observations for two variables, x...Ch. 14.2 - Given are five observations for two variables, x...Ch. 14.2 - Given are five observations collected in a...Ch. 14.2 - Retail and Trade: Female Managers. The following...Ch. 14.2 - Production Line Speed and Quality Control. Brawdy...Ch. 14.2 - The National Football League (NFL) records a...Ch. 14.2 - Sales Experience and Performance. A sales manager...Ch. 14.2 - Broker Satisfaction. The American Association of...Ch. 14.2 - Companies in the U.S. car rental market vary...Ch. 14.2 - Age and the Price of Wine. For a particular red...
Ch. 14.2 - Laptop Ratings. To help consumers in purchasing a...Ch. 14.2 - Stock Beta. In June of 2016, Yahoo Finance...Ch. 14.2 - Distance and Absenteeism. A large city hospital...Ch. 14.2 - Using a global-positioning-system (GPS)-based...Ch. 14.3 - 15. The data from exercise 1...Ch. 14.3 - The data from exercise 2 follow.
The estimated...Ch. 14.3 - Prob. 17ECh. 14.3 - Price and Quality of Headphones. The following...Ch. 14.3 - Sales Experience and Sales Performance. In...Ch. 14.3 - Price and Weight of Bicycles. Bicycling, the...Ch. 14.3 - Cost Estimation. An important application of...Ch. 14.3 - 22. Refer to exercise 9, where the following data...Ch. 14.5 - The data from exercise 1 follow.
Compute the mean...Ch. 14.5 - The data from exercise 2 follow.
Compute the mean...Ch. 14.5 - The data from exercise 3 follow.
What is the...Ch. 14.5 - Prob. 26ECh. 14.5 - To identify high-paying jobs for people who do not...Ch. 14.5 - Broker Satisfaction Conclusion. In exercise 8,...Ch. 14.5 - Cost Estimation Conclusion. Refer to exercise 21,...Ch. 14.5 - Significance of Fleet Size on Rental Car Revenue....Ch. 14.5 - Significance of Racing Bike Weight on Price. In...Ch. 14.6 - 32. The data from exercise 1...Ch. 14.6 - 33. The data from exercise 2...Ch. 14.6 - Prob. 34ECh. 14.6 - 35. The following data are the monthly salaries y...Ch. 14.6 - 36. In exercise 7, the data on y = annual sales ($...Ch. 14.6 - In exercise 5, the following data on x = the...Ch. 14.6 - Prob. 38ECh. 14.6 - 39. In exercise 12, the following data on x =...Ch. 14.7 - The commercial division of a real estate firm...Ch. 14.7 - Following is a portion of the regression output...Ch. 14.7 - Prob. 43ECh. 14.7 - Auto Racing Helmet. Automobile racing,...Ch. 14.8 - Prob. 45ECh. 14.8 - Prob. 46ECh. 14.8 - Prob. 47ECh. 14.8 - Prob. 48ECh. 14.8 - Prob. 49ECh. 14.9 - Consider the following data for two variables, x...Ch. 14.9 - Prob. 51ECh. 14.9 - Predicting Charity Expenses. Charity Navigator is...Ch. 14.9 - Many countries, especially those in Europe, have...Ch. 14.9 - Valuation of a Major League Baseball Team. The...Ch. 14 - The Dow Jones Industrial Average (DJIA) and the...Ch. 14 - Home Sire and Price. Is the number of square feet...Ch. 14 - Online Education. One of the biggest changes in...Ch. 14 - Machine Maintenance. Jensen Tire & Auto is in the...Ch. 14 - Bus Maintenance. The regional transit authority...Ch. 14 - Studying and Grades. A marketing professor at...Ch. 14 - Used Car Mileage and Price. The Toyota Camry is...Ch. 14 - One measure of the risk or volatility of an...Ch. 14 - As part of a study on transportation safety, the...Ch. 14 - Consumer Reports tested 166 different...Ch. 14 - When trying to decide what car to buy, real value...Ch. 14 - Buckeye Creek Amusement Park is open from the...
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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_forwardWhat is the effect of this violation on the regression model? "The number of observations n is less than or equal to the number of parameters to be estimated"arrow_forwardThe Wall Street Journal asked Concur Technologies, Inc., an expense management company, to examine data from 8.3 million expense reports to provide insights regarding business travel expenses. Their analysis of the data showed that New York was the most expensive city. The following table shows the average daily hotel room rate (X) and the average amount spent on entertainment (Y) for a random sample of 9 of the 25 most-visited U.S. cities. These data lead to the estimated regression equation y = 17.49 + 1.0334x. For these data SSE = 1541.4. Use Table 1 of Appendix B. a. Predict the amount spent on entertainment for a particular city that has a daily room rate of $89 (to 2 decimals). b. Develop a 95% confidence interval for the mean amount spent on entertainment for all cities that have a daily room rate of $89 (to 2 decimals). c. The average room rate in Chicago is $128. Develop a 95% prediction interval for the amount spent on entertainment in Chicago (to 2 decimals).arrow_forward
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