2. Predicting Life ExpectancyWe have attempted 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). Note that the Internet value is missing for South Sudan, so the model is fit with the other 49 countries. The table below shows some computer output from fitting this model. . Estimate Std. Error t-value Pr ( > t ) (Intercept) 76.24 4.83 15.77 0.0000 Health 0.131 0.222 0.59 0.5557 Population -0.0003 0.0128 -0.03 0.9794 Internet 0.1118 0.0438 2.55 0.0142 BirthRate -0.594 0.122 -4.86 0.00002 Country Population Health BirthRate LifeExpectancy Iceland 0.324 15.76 13.4 83.1 Kazakhstan 17.035 10.90 22.7 70.5 Lesotho 2.074 14.48 27.5 49.3 Uzbekistan 30.243 9.68 22.5 68.2 Peru 30.376 14.75 19.7 74.8 Iraq 33.417 5.99 31.1 69.5 Nicaragua 6.080 20.89 22.7 74.8 Zambia 14.539 12.57 42.8 58.1 Guinea-Bissau 1.704 7.79 37.5 54.3 Romania 19.981 12.17 8.8 74.5 United States 316.129 20.71 12.5 78.8 Sierra Leone 6.092 11.41 36.6 45.6 Malta 0.423 13.29 9.5 80.7 Luxembourg 0.543 13.64 11.3 81.8 Micronesia, Fed. Sts. 0.104 17.70 23.5 69.0 South Africa 53.157 14.02 20.9 56.7 Panama 3.864 12.79 19.4 77.6 Turkey 74.933 10.74 16.8 75.2 Nigeria 173.615 17.97 41.2 52.5 Hungary 9.894 10.23 9.2 75.3 Albania 2.897 9.85 12.9 77.5 Brunei Darussalam 0.418 7.42 15.5 78.6 Portugal 10.457 12.90 7.9 80.4 Comoros 0.735 7.55 35.2 60.9 Pakistan 182.143 4.73 25.2 66.6 Ethiopia 94.101 16.43 33.0 63.6 Belize 0.332 11.90 23.4 73.9 Sweden 9.600 14.98 11.8 81.7 Bulgaria 7.265 11.67 9.2 74.5 Botswana 2.021 8.75 23.6 47.4 Greece 11.028 11.66 8.5 80.6 Belarus 9.466 13.45 12.5 72.5 United Kingdom 64.107 16.18 12.2 81.0 Tunisia 10.887 13.33 19.8 73.6 Thailand 67.011 17.01 10.2 74.4 Croatia 4.256 12.67 9.4 77.1 Colombia 48.321 16.05 18.8 74.0 Samoa 0.190 17.00 26.2 73.3 Cuba 11.266 13.40 9.5 79.2 Guyana 0.800 13.87 20.3 66.2 South Sudan 11.296 4.00 36.1 55.2 Cote d'Ivoire 20.316 8.51 36.6 50.8 Burkina Faso 16.935 13.52 40.9 56.3 Malaysia 29.717 5.88 17.7 75.0 Australia 23.129 17.75 13.2 82.2 Indonesia 249.866 6.63 18.8 70.8 Kenya 44.354 5.85 34.9 61.7 Ireland 4.598 14.07 15.0 81.0 Grenada 0.106 9.56 19.3 72.7 Korea, Rep. 50.220 11.50 8.6 81.5 a) Which of the variables which are significant at the 5% level? b) Which variable is the most significant predictor of life expectancy in this model? c) Predict the life expectancy of a country that spends 20% of government expenditures on health care, has a population of 2,600,000 , for which 70% of people have access to the Internet, and the birth rate is 33 births per 1000. (Note: government expenditures and people with access to the Internet should be put in as percentage, population as a number in millions and birth rate as a number of births per 1000.) Round your answer to two decimal places.
2. Predicting Life ExpectancyWe have attempted 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). Note that the Internet value is missing for South Sudan, so the model is fit with the other 49 countries. The table below shows some computer output from fitting this model. . Estimate Std. Error t-value Pr ( > t ) (Intercept) 76.24 4.83 15.77 0.0000 Health 0.131 0.222 0.59 0.5557 Population -0.0003 0.0128 -0.03 0.9794 Internet 0.1118 0.0438 2.55 0.0142 BirthRate -0.594 0.122 -4.86 0.00002 Country Population Health BirthRate LifeExpectancy Iceland 0.324 15.76 13.4 83.1 Kazakhstan 17.035 10.90 22.7 70.5 Lesotho 2.074 14.48 27.5 49.3 Uzbekistan 30.243 9.68 22.5 68.2 Peru 30.376 14.75 19.7 74.8 Iraq 33.417 5.99 31.1 69.5 Nicaragua 6.080 20.89 22.7 74.8 Zambia 14.539 12.57 42.8 58.1 Guinea-Bissau 1.704 7.79 37.5 54.3 Romania 19.981 12.17 8.8 74.5 United States 316.129 20.71 12.5 78.8 Sierra Leone 6.092 11.41 36.6 45.6 Malta 0.423 13.29 9.5 80.7 Luxembourg 0.543 13.64 11.3 81.8 Micronesia, Fed. Sts. 0.104 17.70 23.5 69.0 South Africa 53.157 14.02 20.9 56.7 Panama 3.864 12.79 19.4 77.6 Turkey 74.933 10.74 16.8 75.2 Nigeria 173.615 17.97 41.2 52.5 Hungary 9.894 10.23 9.2 75.3 Albania 2.897 9.85 12.9 77.5 Brunei Darussalam 0.418 7.42 15.5 78.6 Portugal 10.457 12.90 7.9 80.4 Comoros 0.735 7.55 35.2 60.9 Pakistan 182.143 4.73 25.2 66.6 Ethiopia 94.101 16.43 33.0 63.6 Belize 0.332 11.90 23.4 73.9 Sweden 9.600 14.98 11.8 81.7 Bulgaria 7.265 11.67 9.2 74.5 Botswana 2.021 8.75 23.6 47.4 Greece 11.028 11.66 8.5 80.6 Belarus 9.466 13.45 12.5 72.5 United Kingdom 64.107 16.18 12.2 81.0 Tunisia 10.887 13.33 19.8 73.6 Thailand 67.011 17.01 10.2 74.4 Croatia 4.256 12.67 9.4 77.1 Colombia 48.321 16.05 18.8 74.0 Samoa 0.190 17.00 26.2 73.3 Cuba 11.266 13.40 9.5 79.2 Guyana 0.800 13.87 20.3 66.2 South Sudan 11.296 4.00 36.1 55.2 Cote d'Ivoire 20.316 8.51 36.6 50.8 Burkina Faso 16.935 13.52 40.9 56.3 Malaysia 29.717 5.88 17.7 75.0 Australia 23.129 17.75 13.2 82.2 Indonesia 249.866 6.63 18.8 70.8 Kenya 44.354 5.85 34.9 61.7 Ireland 4.598 14.07 15.0 81.0 Grenada 0.106 9.56 19.3 72.7 Korea, Rep. 50.220 11.50 8.6 81.5 a) Which of the variables which are significant at the 5% level? b) Which variable is the most significant predictor of life expectancy in this model? c) Predict the life expectancy of a country that spends 20% of government expenditures on health care, has a population of 2,600,000 , for which 70% of people have access to the Internet, and the birth rate is 33 births per 1000. (Note: government expenditures and people with access to the Internet should be put in as percentage, population as a number in millions and birth rate as a number of births per 1000.) Round your answer to two decimal places.
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 25PPS
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2. Predicting Life Expectancy
We have attempted 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). Note that the Internet value is missing for South Sudan, so the model is fit with the other 49 countries. The table below shows some computer output from fitting this model.
| . | Estimate | Std. Error | t-value | Pr ( > t ) |
|---|
| (Intercept) | 76.24 | 4.83 | 15.77 | 0.0000 |
|---|---|---|---|---|
| Health | 0.131 | 0.222 | 0.59 | 0.5557 |
| Population | -0.0003 | 0.0128 | -0.03 | 0.9794 |
| Internet | 0.1118 | 0.0438 | 2.55 | 0.0142 |
| BirthRate | -0.594 | 0.122 | -4.86 | 0.00002 |
| Country | Population | Health | BirthRate | LifeExpectancy |
| Iceland | 0.324 | 15.76 | 13.4 | 83.1 |
| Kazakhstan | 17.035 | 10.90 | 22.7 | 70.5 |
| Lesotho | 2.074 | 14.48 | 27.5 | 49.3 |
| Uzbekistan | 30.243 | 9.68 | 22.5 | 68.2 |
| Peru | 30.376 | 14.75 | 19.7 | 74.8 |
| Iraq | 33.417 | 5.99 | 31.1 | 69.5 |
| Nicaragua | 6.080 | 20.89 | 22.7 | 74.8 |
| Zambia | 14.539 | 12.57 | 42.8 | 58.1 |
| Guinea-Bissau | 1.704 | 7.79 | 37.5 | 54.3 |
| Romania | 19.981 | 12.17 | 8.8 | 74.5 |
| United States | 316.129 | 20.71 | 12.5 | 78.8 |
| Sierra Leone | 6.092 | 11.41 | 36.6 | 45.6 |
| Malta | 0.423 | 13.29 | 9.5 | 80.7 |
| Luxembourg | 0.543 | 13.64 | 11.3 | 81.8 |
| Micronesia, Fed. Sts. | 0.104 | 17.70 | 23.5 | 69.0 |
| South Africa | 53.157 | 14.02 | 20.9 | 56.7 |
| Panama | 3.864 | 12.79 | 19.4 | 77.6 |
| Turkey | 74.933 | 10.74 | 16.8 | 75.2 |
| Nigeria | 173.615 | 17.97 | 41.2 | 52.5 |
| Hungary | 9.894 | 10.23 | 9.2 | 75.3 |
| Albania | 2.897 | 9.85 | 12.9 | 77.5 |
| Brunei Darussalam | 0.418 | 7.42 | 15.5 | 78.6 |
| Portugal | 10.457 | 12.90 | 7.9 | 80.4 |
| Comoros | 0.735 | 7.55 | 35.2 | 60.9 |
| Pakistan | 182.143 | 4.73 | 25.2 | 66.6 |
| Ethiopia | 94.101 | 16.43 | 33.0 | 63.6 |
| Belize | 0.332 | 11.90 | 23.4 | 73.9 |
| Sweden | 9.600 | 14.98 | 11.8 | 81.7 |
| Bulgaria | 7.265 | 11.67 | 9.2 | 74.5 |
| Botswana | 2.021 | 8.75 | 23.6 | 47.4 |
| Greece | 11.028 | 11.66 | 8.5 | 80.6 |
| Belarus | 9.466 | 13.45 | 12.5 | 72.5 |
| United Kingdom | 64.107 | 16.18 | 12.2 | 81.0 |
| Tunisia | 10.887 | 13.33 | 19.8 | 73.6 |
| Thailand | 67.011 | 17.01 | 10.2 | 74.4 |
| Croatia | 4.256 | 12.67 | 9.4 | 77.1 |
| Colombia | 48.321 | 16.05 | 18.8 | 74.0 |
| Samoa | 0.190 | 17.00 | 26.2 | 73.3 |
| Cuba | 11.266 | 13.40 | 9.5 | 79.2 |
| Guyana | 0.800 | 13.87 | 20.3 | 66.2 |
| South Sudan | 11.296 | 4.00 | 36.1 | 55.2 |
| Cote d'Ivoire | 20.316 | 8.51 | 36.6 | 50.8 |
| Burkina Faso | 16.935 | 13.52 | 40.9 | 56.3 |
| Malaysia | 29.717 | 5.88 | 17.7 | 75.0 |
| Australia | 23.129 | 17.75 | 13.2 | 82.2 |
| Indonesia | 249.866 | 6.63 | 18.8 | 70.8 |
| Kenya | 44.354 | 5.85 | 34.9 | 61.7 |
| Ireland | 4.598 | 14.07 | 15.0 | 81.0 |
| Grenada | 0.106 | 9.56 | 19.3 | 72.7 |
| Korea, Rep. | 50.220 | 11.50 | 8.6 | 81.5 |
a) Which of the variables which are significant at the 5% level?
b) Which variable is the most significant predictor of life expectancy in this model?
c) Predict the life expectancy of a country that spends 20% of government expenditures on health care, has a population of 2,600,000 , for which 70% of people have access to the Internet, and the birth rate is 33 births per 1000. (Note: government expenditures and people with access to the Internet should be put in as percentage, population as a number in millions and birth rate as a number of births per 1000.) Round your answer to two decimal places.
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