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
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
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