Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 9. 8 11 9. 11 14 4 12 y 7.47 6.66 13.28 7.1 7.59 8.43 6.09 5.39 7.9 6.48 5.65 OA. OB. Oc. OD. Ay 25- Ay 25- Ay 25- Ay 25- 20 20 20 20 155 155 155 15- to 10- 105 10- 10- 53 55 55 0- O 5 10 15 20 25 0- Ó 5 10 15 20 25 0- O 5 10 15 20 25 -> 05 10 15 20 25 Find the equation of the regression line. (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.) Identify a characteristic of the data that is ignored by the regression line. O A. There is an influential point that strongly affects the graph of the regression line. B. There is no characteristic of the data that is ignored by the regression line. OC. The data has a pattern that is not a straight line. O D. There is no trend in the data.

Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 9. 8 11 9. 11 14 4 12 y 7.47 6.66 13.28 7.1 7.59 8.43 6.09 5.39 7.9 6.48 5.65 OA. OB. Oc. OD. Ay 25- Ay 25- Ay 25- Ay 25- 20 20 20 20 155 155 155 15- to 10- 105 10- 10- 53 55 55 0- O 5 10 15 20 25 0- Ó 5 10 15 20 25 0- O 5 10 15 20 25 -> 05 10 15 20 25 Find the equation of the regression line. (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.) Identify a characteristic of the data that is ignored by the regression line. O A. There is an influential point that strongly affects the graph of the regression line. B. There is no characteristic of the data that is ignored by the regression line. OC. The data has a pattern that is not a straight line. O D. There is no trend in the data.

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Description: Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line.9.8119.1114412y 7.476.6613.287.17.598.436.095.397.96.485.65O A.OB.Oc.OD.Ay25-Ay2

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?
The 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.
Sarah is the office manager for a group of financial advisors who provide financial services for individual clients. She would like to investigate whether a relationship exists between the number of presentations made to prospective clients in a month and the number of new clients per month. The following table shows the number of presentations and corresponding new clients for a random sample of six employees. Employee Presentations New Clients 1 7 2 2 9 3 3 9 4 4 10 3 5 11 5 6 12 3 Sarah would like to use simple regression analysis to estimate the number of new clients per month based on the number of presentations made by the employee per month. The expected number of new clients per month for an employee who made 10 presentations per month is ________. 2.3982 1.6753 3.0521 3.4348
41. Which of the following is the multiple regression model for the data? (a) y = -0.11050 + 2.10797x1 + 0.40717x2 (b) y = -0.11050 + 0.40717x1 + 2.10797x2 (c) y = 0.23563 + 0.00062x1 + 0.23626x2 (d) none (e) y = -0.44187 + 2.42120x1 + 0.36135x2
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=92.3−0.669x
Bill is the office manager for a group of financial advisors who provide financial services for individual clients. She would like to investigate whether a relationship exists between the number of presentations made to prospective clients in a month and the number of new clients per month. The following table shows the number of presentations and corresponding new clients for a random sample of six employees. Employee Presentations New Clients 1 2 1 2 8 2 3 9 4 4 10 3 5 11 5 6 12 6 Bill would like to use simple regression analysis to estimate the number of new clients per month based on the number of presentations made by the employee per month. The average number of new clients per month for an employee who made 20 presentations per month is ________. 5.02 5.45 3.43 8.69
Find two lines of regressions from the following Data: Age of husband : 25, 21, 22.5, 26.3, 35.7, 20.8, 22.9, 40.6, 25.2, 18.1 Age of wife: 16.3, 15.1, 20.9, 17.0, 22.6, 14.3, 18.9, 21.0, 15.6, 14.8 Estimate the following: a. The age of husband when age of wife is 21.5 b. Age of wife when age of husband is 45.9 c. The correlation coefficient.
A sales manager collected the following data on y = annual sales and x = years of experience. The estimated regression equation for these data is y = 80 + 4x. Click on the datafile logo to reference the data. (NEED ANSWERS FOR A, B, C) a. Compute SST, SSR, and SSE. b. Compute the coefficient of determination r2. Comment on the goodness of fit. __________ (provides a good fit / does not produce much of a fit) c. What is the value of the sample correlation coefficient (to 2 decimals)?
Which of the following best describes the difference between the interpretation of the correlation coefficient and the beta coefficient for a regression equation expressing the relationship between variables X and Y? A) The beta coefficient represents the linear relationship between X and Y, whereas the correlation coefficient represents the percent variability in Y explained by XB) The beta coefficient represents the difference between observed and expected values of Y, whereas the correlation coefficient represents the linear relationship between X and Y.C) The beta coefficient predicts increases or decreases in Y with increases or decreases in X, whereas the correlation coefficient provides a unit-free measure of the strength of the relationship.D) The correlation coefficient predicts increases or decreases in Y with increases or decreases in X, whereas the beta coefficient provides a unit-free measure of the strength of the relationship.
In the packaging department of a large aircraft parts distributor, a fairly reliable estimate of packaging and processing costs can be determined by knowing the weight of an order. Thus, the weight is a cost driver that accounts for a sizable fraction of the packaging and processing costs at this company. Data for the past 10 orders are given as follows: a. Estimate the b0 and b1 coefficients, and determine the linear regression equation to fit these data. b. What is the correlation coefficient (R)? c. If an order weighs 205 lb, how much should it cost to package and process it? Packaging and Processing Cost ($), y Weight (Pounds), x 99 240 111 275 86 210 85 185 121 315 114 300 111 285 100 270 109 275 85 200
Consider the data below. Suppose that in the aerobic exercise group we also measured the number of hours of aerobic exercise per week; the mean is 5.2 hours with a standard deviation of 2.1 hours. The sample correlation is -0.42. a) Estimate the equation of the regression line that best describes the relationship between number of hours of exercise per week and HDL cholesterol level (Assume that the dependent variable is HDL level). b) Estimate the HDL level for a person who exercises 7 hours per week. c) Estimate the HDL level for a person who does not exercise. Exercise Group N Mean Standard Deviation Weight Training 20 49.7 10.2 Aerobic Exercise 20 43.1 11.1 Stretching/Yoga 20 57.0 12.5
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. x 7 4 12 3 6 10 8 13 11 5 9 y 14.61 7.56 18.76 4.45 12.64 18.24 16.21 18.45 18.70 10.30 17.41 y^=???+1.41.4x (Round to two decimal places as needed.)