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, Problem 62SE
In a manufacturing process the assembly line speed (feet per minute) was thought to affect the number of defective parts found during the inspection process. To test this theory, managers devised a situation in which the same batch of parts was inspected visually at a variety of line speeds. They collected the following data.
Line Speed | Number of Defective Parts Found |
20 | 21 |
20 | 19 |
40 | 15 |
30 | 16 |
60 | 14 |
40 | 17 |
- a. Develop the estimated regression equation that relates line speed to the number of defective parts found.
- b. At a .05 level of significance, determine whether line speed and number of defective parts found are related.
- c. Did the estimated regression equation provide a good fit to the data?
- d. Develop a 95% confidence interval to predict the
mean number of defective parts for a line speed of 50 feet per minute.
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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
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 - The following data give the percentage of women...Ch. 14.2 - Brawdy Plastics, Inc., produces plastic seat belt...Ch. 14.2 - The National Football League (NFL) records a...Ch. 14.2 - A sales manager collected the following data on...Ch. 14.2 - The American Association of Individual Investors...Ch. 14.2 - Prob. 9ECh. 14.2 - On March 31, 2009, Ford Motor Companys shares were...
Ch. 14.2 - To help consumers in purchasing a laptop computer,...Ch. 14.2 - Concur Technologies, Inc., is a large...Ch. 14.2 - To the Internal Revenue Service, the...Ch. 14.2 - A large city hospital conducted a study to...Ch. 14.3 - The data from exercise 1 follow. xi 1 2 3 4 5 yi 3...Ch. 14.3 - The data from exercise 2 follow. xi 3 12 6 20 14...Ch. 14.3 - The data from exercise 3 follow. xi 2 6 9 13 20 yi...Ch. 14.3 - The following data show the brand, price (), and...Ch. 14.3 - In exercise 7 a sales manager collected the...Ch. 14.3 - Bicycling, the worlds leading cycling magazine,...Ch. 14.3 - An important application of regression analysis in...Ch. 14.3 - Refer to exercise 9, where the following data were...Ch. 14.5 - The data from exercise 1 follow. xi 1 2 3 4 5 yi 3...Ch. 14.5 - The data from exercise 2 follow. xi 3 12 6 20 14...Ch. 14.5 - The data from exercise 3 follow. xi 2 6 9 13 20 yi...Ch. 14.5 - In exercise 18 the data on price () and the...Ch. 14.5 - To identify high-paying jobs for people who do not...Ch. 14.5 - In exercise 8 ratings data on x = the quality of...Ch. 14.5 - Refer to exercise 21, where data on production...Ch. 14.5 - Refer to exercise 9, where the following data were...Ch. 14.5 - In exercise 20, data on x = weight (pounds) and y...Ch. 14.6 - The data from exercise 1 follow. xi 1 2 3 4 5 yi 3...Ch. 14.6 - The data from exercise 2 follow. xi 3 12 6 20 14...Ch. 14.6 - The data from exercise 3 follow. xi 2 6 9 13 20 yi...Ch. 14.6 - The following data are the monthly salaries y and...Ch. 14.6 - In exercise 7, the data on y = annual sales (...Ch. 14.6 - In exercise 13, data were given on the adjusted...Ch. 14.6 - Refer to exercise 21, where data on the production...Ch. 14.6 - In exercise 12, the following data on x = average...Ch. 14.7 - The commercial division of a real estate firm is...Ch. 14.7 - Following is a portion of the computer output for...Ch. 14.7 - A regression model relating x, number of...Ch. 14.7 - A 2012 suvey conducted by Idea Works provided data...Ch. 14.7 - Automobile racing, high-performance driving...Ch. 14.8 - Given are data for two variables, x and y. xi 6 11...Ch. 14.8 - The following data were used in a regression...Ch. 14.8 - Data on advertising expenditures and revenue (in...Ch. 14.8 - Refer to exercise 7, where an estimated regression...Ch. 14.8 - In 2011 home prices and mortgage rates dropped so...Ch. 14.9 - Consider the following data for two variables, x...Ch. 14.9 - Consider the following data for two variables, x...Ch. 14.9 - Charity Navigator is Americas leading independent...Ch. 14.9 - Many countries, especially those in Europe, have...Ch. 14.9 - Prob. 54ECh. 14 - Does a high value of r2 imply that two variables...Ch. 14 - In your own words, explain the difference between...Ch. 14 - What is the purpose of testing whether 1 = 0? If...Ch. 14 - The Dow Jones Industrial Average (DJIA) and the...Ch. 14 - Is the number of square feet of living space a...Ch. 14 - One of the biggest changes in higher education in...Ch. 14 - Jensen Tire Auto is in the process of deciding...Ch. 14 - In a manufacturing process the assembly line speed...Ch. 14 - A sociologist was hired by a large city hospital...Ch. 14 - The regional transit authority for a major...Ch. 14 - A marketing professor at Givens College is...Ch. 14 - The Transactional Records Access Clearinghouse at...Ch. 14 - The Toyota Camry is one of the best-selling cars...Ch. 14 - You have been assigned to analyze the risk...Ch. 14 - As part of a study on transportation safety, the...Ch. 14 - Consumer Reports tested 166 different...Ch. 14 - Finding the Best Car Value When trying to decide...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_forwardDoes Table 1 represent a linear function? If so, finda linear equation that models the data.arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forward
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