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.

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Description: 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

MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

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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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