In order to study the relation between the number of hours which ten persons studied for a French and their scores on the test, we find that the best linear regression of their scores on the number of hours is: ỹ = 21.69 + 3.471 x where y represents the student's score and x denotes the student's number of hours. If the correlation between the scores and the hours is r= 0.9763 and s= 376. The test statistic for testing whether there is a linear relation between the scores and the hours is O a. 12.759 O b. 3.6 O c. 21.27 O d. 1.79

In order to study the relation between the number of hours which ten persons studied for a French and their scores on the test, we find that the best linear regression of their scores on the number of hours is: ỹ = 21.69 + 3.471 x where y represents the student's score and x denotes the student's number of hours. If the correlation between the scores and the hours is r= 0.9763 and s= 376. The test statistic for testing whether there is a linear relation between the scores and the hours is O a. 12.759 O b. 3.6 O c. 21.27 O d. 1.79

Name: 9 In order to study the relation between the number of hours which ten
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Description: 9 In order to study the relation between the number of hours which ten persons studied for a French andtheir scores on the test, we find that the best linear regression of their scores on the number of hoursis:ý = 21.69 + 3.471 x wherey represents th

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section4.3: Fitting Linear Models To Data

Problem 34SE: For the following exercises, consider this scenario: The profit of a company decreased steadily...

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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For the following exercises, consider this scenario: The profit of a company decreased steadily overa ten-year spam.The following ordered pairs shows dollars and the number of units sold in hundreds and the profit in thousands ofover the ten-year span, (number of units sold, profit) for specific recorded years: (46,600),(48,550),(50,505),(52,540),(54,495). Use linear regression to determine a function Pwhere the profit in thousands of dollars depends onthe number of units sold in hundreds.
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?