The graph below includes the results of a linear regression using this data. (For example, the client identified with the grey square in the graph has a BMI of 29.28 and has cost the insurance company $22,411 over the previous 60 months.) Insurance Charges ($$) 70000 60000 50000 40000 30000 20000 10000 0 Males Smokers, aged 40 to 64 y = 1487x-9399.7 R² = 0.6501 18 23 28 33 38 43 BMI Another client is indicated by a large black triangle on the graph. Would the residual of the prediction for this client be positive or negative? Briefly explain how you would know without using arithmetic. The graph below includes the results of a linear regression using this data. (For example, the client identified with the grey square in the graph has a BMI of 29.28 and has cost the insurance company $22,411 over the previous 60 months.) Insurance Charges ($$) 70000 60000 50000 40000 30000 20000 10000 0 Males Smokers, aged 40 to 64 y= 1487x-9399.7 R² = 0.6501 18 23 28 33 38 43 BMI One client is indicated by the grey diamond on the graph. What is the error (residual) of the prediction for the dollar amount of claims for this client with a BMI of 29.28? Briefly type out how you got this answer.
The graph below includes the results of a linear regression using this data. (For example, the client identified with the grey square in the graph has a BMI of 29.28 and has cost the insurance company $22,411 over the previous 60 months.) Insurance Charges ($$) 70000 60000 50000 40000 30000 20000 10000 0 Males Smokers, aged 40 to 64 y = 1487x-9399.7 R² = 0.6501 18 23 28 33 38 43 BMI Another client is indicated by a large black triangle on the graph. Would the residual of the prediction for this client be positive or negative? Briefly explain how you would know without using arithmetic. The graph below includes the results of a linear regression using this data. (For example, the client identified with the grey square in the graph has a BMI of 29.28 and has cost the insurance company $22,411 over the previous 60 months.) Insurance Charges ($$) 70000 60000 50000 40000 30000 20000 10000 0 Males Smokers, aged 40 to 64 y= 1487x-9399.7 R² = 0.6501 18 23 28 33 38 43 BMI One client is indicated by the grey diamond on the graph. What is the error (residual) of the prediction for the dollar amount of claims for this client with a BMI of 29.28? Briefly type out how you got this answer.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Question
Q1.8, I need help for both please
Transcribed Image Text:The graph below includes the results of a linear regression using this data. (For example, the client
identified with the grey square in the graph has a BMI of 29.28 and has cost the insurance
company $22,411 over the previous 60 months.)
Insurance Charges ($$)
70000
60000
50000
40000
30000
20000
10000
0
Males Smokers, aged 40 to 64
y = 1487x-9399.7
R² = 0.6501
18
23
28
33
38
43
BMI
Another client is indicated by a large black triangle on the graph. Would the residual of the
prediction for this client be positive or negative? Briefly explain how you would know without
using arithmetic.
Transcribed Image Text:The graph below includes the results of a linear regression using this data. (For example, the client
identified with the grey square in the graph has a BMI of 29.28 and has cost the insurance
company $22,411 over the previous 60 months.)
Insurance Charges ($$)
70000
60000
50000
40000
30000
20000
10000
0
Males Smokers, aged 40 to 64
y= 1487x-9399.7
R² = 0.6501
18
23
28
33
38
43
BMI
One client is indicated by the grey diamond on the graph. What is the error (residual) of the
prediction for the dollar amount of claims for this client with a BMI of 29.28? Briefly type out how
you got this answer.
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