Q1.5 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 = 0.6501182328333843BMIInterpret the value of the coefficient of determination in the context of this problem. (See yourlecture notes.)

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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

Section4.6: Regression And Median-fit Lines

Problem 4PPS

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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?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
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
The follow table gives the approximate economic value associated with various levels of oil recovery in Texas. Find the regression line, and use it to estimate the economic value associated with a recovery level of 70%.
When a linear regression for the growth of the number of employees in the health care and social assistance fields in Quebec from 2010 to 2014 is performed, the following values are determined: r=0.9948 r2=0.9897 These values indicate that a linear model is a good fit for the data. True False

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