The least-square regression line for the given data is y = 0.449x - 30.27. Determine the residual of a data point for whichx = 90 and y=10, rounding to three decimal places.Temperature, xNumber of absences, yOA. -0.14OB. 20.14C. 115.78OD. 10.1472385791109010888981575 10041580-5 Theleast-square regression line for the given data is y=2.097x-0.552. Determine the residual of a data point for whichx = 0 and y=-1, rounding to three decimal places.X-5-341- 1y- 10-89- 2OA. - 0.552OB. -0.448OC. 2.649OD. -1.5521-2-60-12336-40-8

The given least-square regression line is y^=0.449x-30.27.

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Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 5PPS

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Find the mean hourly cost when the cell phone described above is used for 240 minutes.
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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The least-square regression line for the given data is y = 0.449x-30.27. Determine the residual of a data point for which x= 90 and y=10, rounding to three decimal places. Temperature, x Number of absences, y OA. -0.14 B. 20.14 C. 115.78 OD. 10.14 72 3 85 7 91 10 90 10 88 8 98 75 100 15 4 15 80 5