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Life insurance companies are keenly interested in predicting how long their customers are  likely to live, because this will determine their premiums and ultimately their profitability.  An Australian life insurance company is interested in the relationship, if any, between the age  at death of their male customers and that of the customer’s father. Data are collected on a  random sample of 100 of their male customers who have recently died. The customer’s age at  death was plotted against that of their father and a linear regression model applied. Relevant  output is shown below Examine both the scatterplot and the correlation matrix provided above. Comment on the  apparent relationship between the customer’s age at death and their father’s age at death in  the plot. Explain how the information in the correlation matrix supports your conclusion

Life insurance companies are keenly interested in predicting how long their customers are  likely to live, because this will determine their premiums and ultimately their profitability.  An Australian life insurance company is interested in the relationship, if any, between the age  at death of their male customers and that of the customer’s father. Data are collected on a  random sample of 100 of their male customers who have recently died. The customer’s age at  death was plotted against that of their father and a linear regression model applied. Relevant  output is shown below Examine both the scatterplot and the correlation matrix provided above. Comment on the  apparent relationship between the customer’s age at death and their father’s age at death in  the plot. Explain how the information in the correlation matrix supports your conclusion

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Life insurance companies are keenly interested in predicting how long their customers are 
likely to live, because this will determine their premiums and ultimately their profitability. 
An Australian life insurance company is interested in the relationship, if any, between the age 
at death of their male customers and that of the customer’s father. Data are collected on a 
random sample of 100 of their male customers who have recently died. The customer’s age at 
death was plotted against that of their father and a linear regression model applied. Relevant 
output is shown below

Examine both the scatterplot and the correlation matrix provided above. Comment on the 
apparent relationship between the customer’s age at death and their father’s age at death in 
the plot. Explain how the information in the correlation matrix supports your conclusion

Customer's Age (Death) 100 90 80 70 60 50 40 40 Customer's Age at Death V Father's Age 50 Father's Age (Death) Customer's Age (Death) 60 70 Father's Age (Death) 80 0.664342737 90 Father's Age (Death) Customer's Age (Death) 1 100
Transcribed Image Text:Customer's Age (Death) 100 90 80 70 60 50 40 40 Customer's Age at Death V Father's Age 50 Father's Age (Death) Customer's Age (Death) 60 70 Father's Age (Death) 80 0.664342737 90 Father's Age (Death) Customer's Age (Death) 1 100
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