Q1.6 The graph below includes the results of a linear regression using this data. (For example, the clientidentified with the grey square in the graph has a BMI of 29.28 and has cost the insurancecompany $22,411 over the previous 60 months.)Insurance Charges ($$)700006000050000400003000020000100000Males Smokers, aged 40 to 64y = 1487x-9399.7R² = 0.6501182328333843BMIUse the regression equation to predict the dollar amount of claims for a client with a BMI of 29.28.Write your answer as a number and do NOT round. Please do not use dollar signs, equaul signs,commas, or any other symbols!

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Answered: The graph below includes the results of…

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 2SGR

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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?
please dont just say constant decrease. what does the context of that value mean?
The graph below includes the results of a linear regression using this data. (For example, the client identified with the triangle in the graph has a BMI of 30.36 and has cost the insurance company $62,592 over the previous 5 years.) Insurance Charges ($$) 70000 60000 50000 40000 30000 20000 10000 0 18 23 Males Smokers, aged 40 to 64 28 BMI 33 y = 1487x - 9399.7 R² = 0.6501 38 43 Is the intercept practical in the context of this problem? Briefly explain why or why not.
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs = 247733.3 +(43.2000) x Billable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) b. Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, bo. if appropriate. Choose the correct answer below. OA. The value of by is the predicted overhead costs for 0 billable hours. OB. For each increase of 1 unit in overhead costs, the predicted billable hours are estimated to increase by bo OC. It is not appropriate to interpret by. because its value is the predicted…
The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. What proportion of variation in the number of complaints can be explained by hourly wages? From the results shown above, write the regression equation If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?
The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive? Is the regression coefficient statistically significant? How can you tell?
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c. E Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs = 5304.4 + (0.0219) × Billable Hours +1 (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) Monthly overhead costs and billable hours data Overhead Costs Billable Hours O IT $385,000 3,000 $430,000 4,000 $460,000 5,000 $511,000 6,000 $580,000 7,000 $600,000 8,000 Print Done
Midgett Co. has accumulated data to use in preparing its annual profit plan for the upcoming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff suggested that linear regression be employed to derive an equation for maintenance hours and costs. Data regarding the maintenance hours and costs for the last year and the results of the regression analysis are as follows: Month MaintenanceCost Machine Hours Jan. $ 5,040 620 Feb. 3,648 420 Mar. 4,320 520 Apr. 3,331 390 May 5,221 650 June 3,550 400 July 3,655 430 Aug. 5,365 690 Sept. 5,110 640 Oct. 4,866 610 Nov. 3,944 460 Dec. 3,790 440 Sum $ 51,840 6,270 Average $ 4,320.00 522.50 Average cost per hour ($51,840/6,270) = $8.27 (rounded to the nearest cent)r = 0.99821r2 = 0.99780The percent of the total variance that can be…
Armer Company is accumulating data to use in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested the use of linear regression to derive an equation for maintenance hours and costs. Data regarding the maintenance hours and costs for the last year and the results of the regression analysis follow: Month Maintenance Cost Machine Hours Jan. $ 5,050 820 Feb. 3,850 660 Mar. 4,450 740 Apr. 3,670 640 May 5,200 840 June 3,810 650 July 3,880 660 Aug. 5,320 940 Sept. 5,110 830 Oct. 4,900 810 Nov. 4,150 690 Dec. 4,010 680 Sum $ 53,400 8,960 Average $ 4,450 $ 747 Average cost per hour $ 6.00 a (intercept) $ 194.33 b (coefficient) 6.2201 Standard error of the estimate 144.247 R-squared 0.9517 t-value for b 14.030…
According to human capital theory, a person’s earning is linked to her level of education – there is a relationship between workers income and years of education. Using data from the Labour Force Survey, a researcher found the following regression results for earnings on intercept, years of education, experience, and experience squared: Earnings = 5.24 + 0.035 educ + 0.165 exper – 0.003 exper2 (2.45) (0.012) (0.031) (0.001) Construct a 95% confidence interval for the effect of years of education on earnings ? 2. Consider an individual with 8 years of experience. What would you expect to be the return to two (2) additional years of experience (the effect on earnings)? 3. According to economic theory,…

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