Use the given data to find the best predicted value of the response variable. Six pairs of data yield r = 0.789 and the regression equation y = 4x – 2. Also, y= 19.0. What is the best predicted value of y for x = 5? O 18.0 19.0 O 18.5 O 22.0
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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.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?For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.
- Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. Managers rate employees according to job performance and attitude. The results for several randomly selected employees are given below. Performance Attitute59 7263 6765 7869 8258 7577 8776 9269 8370 8764 78 A. y=11.7+1.02x B. y=2.81+1.35x C. y=−47.3+2.02x D. y=92.3−0.669xThe personnel director of a large hospital is interested in determining the relationship (if any) between an employee’s age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 9 10Age 30 50 40 55 30 28 60 25 30 45Sick Days 7 4 3 2 9 10 0 8 5 2 The estimated regression equation and the standard error are given. Sick Days=14.310162−0.236900(Age) Se=1.682207 Find the 95% prediction interval for the average number of sick days an employee will take per year, given the employee is 34 . Round your answer to two decimal places.Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary Table 7 x 24 26 28 30 32 y 15 13 20 16 24
- Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. X 1, 3, 5, 7, 9 Y 143, 116, 100, 98, 90Consider the following data set. x 1 2 3 4 5 y 2.2 2 1.5 1.2 1.1 (b) Find the equation of the regression line. (Round the values to two decimal places.) y =The grades of a class of 9 students on a midterm report (x) and on the final examination (y) are as follows: Give the following: a. linear regression line and equation b. computation of the coefficient of determination ?^2 c. Computation of the coefficient of correlation ? d. Estimate the final examination grade of a student who received a grade of 85 on the midterm report.
- Use the following data to a. determine the coefficient of correlation, rounded to the nearest thousandth, b. find the equation of the regression line for time watching TV and time on the Internet, c. approximate how much time on the Internet can we predict for a person who spends 10 hours weekly watching TV.Subject: A B C D E F GTime watching TV: 8, 4, 2, 7, 7, 5, 6Time on the internet: 14, 12, 8, 17, 18, 9, 18 A. a. r = 0.752 b. y = 1.52x + 5.25 c. 20.5 hours B. a. r = -0.752 b. y = -1.52x + 5.25 c. 9.9 hours C. a. r = -0.752 b. y = 5.25x + 1.52 c. 54 hours D. a. r = 0.752 b. y = 1.52x - 5.25 c. 10 hoursIf the standard error of the estimate for a regression model fitted to a large number of paired observations is 1.75, approximately 95% of the residuals would lie within ______. −3.50 and +3.50 −1.75 and +1.75 −0.95 and +0.95 −0.68 and +0.68 −0.97 and +0.97