The percentage of employees who cease their employment during a year is referred to as employee turmover, and it is a serious issue for businesses. The following table shows the cost, in millions of dollars, to a certain company for a given employee tumover percentage in a year. E = employee turnover C= cost 10 20 30 40 200 330 460 590 (a) Show that the data can be modeled by a linear function. For each increase in employee turnover by ten percent the cost increases by ( million dollars. (b) Find the slope of the linear function. million dollars per percentage point (c) Find a linear model for the data. CE) = (d) Use the result from part (c) to find the cost to the company if employee turmover is 22% in a year. million dollars

The percentage of employees who cease their employment during a year is referred to as employee turmover, and it is a serious issue for businesses. The following table shows the cost, in millions of dollars, to a certain company for a given employee tumover percentage in a year. E = employee turnover C= cost 10 20 30 40 200 330 460 590 (a) Show that the data can be modeled by a linear function. For each increase in employee turnover by ten percent the cost increases by ( million dollars. (b) Find the slope of the linear function. million dollars per percentage point (c) Find a linear model for the data. CE) = (d) Use the result from part (c) to find the cost to the company if employee turmover is 22% in a year. million dollars

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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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
bThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.
Does Table 1 represent a linear function? If so, finda linear equation that models the data.
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?