The below data on the production volume x and total cost y (in dollars) for a particular manufacturing operation were used to develop the estimated regression equation ý = 1,401.33 + 7.36x. Production Volume Total Cost (units) ($) 400 4,000 450 5,100 550 5,400 600 6,000 700 6,300 750 7,000 (a) The company's production schedule shows that 500 units must be produced next month. What is the point estimate of the total cost (in dollars) for next month? (Round your answer to the nearest cent.) (b) Develop a 99% prediction interval for the total cost (in dollars) for next month. (Round your answers to the nearest cent.) to $ (c) If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,500, should managers be concerned about incurring such a high total cost for the month? Discuss. Since $6,500 is --Select-- V the prediction interval, managers should --Select--- v concerned about incurring such a high total cost for one month.

The below data on the production volume x and total cost y (in dollars) for a particular manufacturing operation were used to develop the estimated regression equation ý = 1,401.33 + 7.36x. Production Volume Total Cost (units) ($) 400 4,000 450 5,100 550 5,400 600 6,000 700 6,300 750 7,000 (a) The company's production schedule shows that 500 units must be produced next month. What is the point estimate of the total cost (in dollars) for next month? (Round your answer to the nearest cent.) (b) Develop a 99% prediction interval for the total cost (in dollars) for next month. (Round your answers to the nearest cent.) to $ (c) If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,500, should managers be concerned about incurring such a high total cost for the month? Discuss. Since $6,500 is --Select-- V the prediction interval, managers should --Select--- v concerned about incurring such a high total cost for one month.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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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?
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