In Exercises 9.25 and 9.64 we attempt to predict a country€™s life expectancy based on the percent of government expenditure on health care, using a sample of fifty countries in the dataset SampCountries. We now add to the model the variables population (in millions), percentage with Internet, and birth rate (births per 1000). Table 10.1 shows some computer output from fitting this model.

Table 10.1

(a) Which variables are significant predictors of life expectancy in this model, at a 5% level? Which is the most significant?
(b) Predict the life expectancy of a country that spends 20% of government expenditures on health care, has a population of 2,500,000, for which 75% of people have access to the Internet, and the birth rate is 30 births per 1000.
(c) How does predicted life expectancy change if we find out the Internet rate is actually higher?

Exercise 9.25

A random sample of 50 countries is stored in the dataset SampCountries. Two variables in the dataset are life expectancy (LifeExpectancy) and percentage of government expenditure spent on health care (Health) for each country.Weare interested in whether or not the percent spent on health care can be used to effectively predict life expectancy.

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Estimate Std. Error t-value Pr(> [t|) |(Intercept) <2e-16 0.543 0.570 0.023 77.30 4.11 18.788 70.613 Health 0.133 0.216 Population 0.029 0.047 -0.573 -0.017 Internet 0.112 2.347 -4.824 BirthRate -0.555 0.115 <0.0001