How did life expectancy at birth change between 2010 and 2015 in the three coun- tries? [5] Hint: Use the percentage change formula: %∆ = NEW−OLD OLD 2. How did expenditure per person change between 2010 and 2015 in the three coun- tries? [5]
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Data on life expectancy for a sample of countries in 2005 and 2010 2005 2010 Country Life Expectancy at Birth Country Life Expectancy at Birth Canada 80 Canada 81 United States 78 United States 78 Mexico 75 Mexico 76 Columbia 72 Columbia 73 Japan 81 Japan 82 China 72 China 74 Sudan 59 Sudan 61 Kenya 53 Kenya 59 Italy 80 Italy 80 Germany 79 Germany 79 What is the standard deviation for 2005 life expectancy, if (x- negativex)squared 2 = 824.9 Group of answer choices 37.21 82.49 9.08 50.41
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Frequency tables, histograms, and life expectancy rates: The United Nations Development Programme (2015b) published life expectancy rates- the number of years an adult can expect to live for 195 countries around the world. Below is a randomly selected sample of 30 of them. Country Life Expectancy Afghanistan 60.95 Armenia 74.56 Australia 82.50 Belarus 69.93 Bosnia and Herzegovina 76.37 Burkina Faso 56.34 Canada 81.48 Côte d'Ivoire 50.72 Cuba 79.26 Ecuador 76.47 Ghana 61.13 Guyana 66.30 Indonesia 70.83 Japan 83.58 Jordan 73.85 Lebanon 80.01 Malaysia 75.02 Mexico 77.50 Namibia 64.48 New Zealand 81.13 Panama 77.56 Saint Lucia 74.80 Slovakia 75.40 Sweden 81.82 Tonga 72.67 Trinidad and Tobago 69.87 Tunisia 75.87 United Kingdom 80.55 United States 78.94 Zimbabwe 59.87 a. Create a grouped frequency table for these data. b. The data have quite a range, with the lowest life expectancy of 50.72 in Côte d'Ivoire and the highest life expectancy of 83.58 in Japan. What research hypotheses come to mind when you examine these data? State at least one research question that these data suggest to you. c. Create a grouped histogram for these data. As always, be careful when determining the midpoints of the intervals. d. Examine the histogram and give a brief description of the distribution. Are there unusual scores? Are the data symmetric, or are they skewed? If they are skewed, in which direction?
10 Correlation Analysis: It is widely believed that life expectancy is longer in higher income countries than in lower income countries. Suppose that you were able to randomly sample countries where per capita income and life expectancy are known. Is life expectancy positively correlated with per capita income indeed? Test this hypothesis using life expectancy data. 1. What is the null hypothesis and alternate hypothesis? 2. Measure the degree of association between PER_CAPITA and LIFE_EXPEC. That is, what is the Pearson correlation coefficient and Spearman rho, respectively? 3. Draw a conclusion about the relationship between life expectancy and per capita income. COUNTRY Group PER_CAPITA LIFE_EXPEC Angola 4 340 47 Burkina Faso 4 240 47 Burundi 4 180 46 Chad 4 240 48 Gabon 2 4230 54 Lesotho 4 670 56 Madagascar 4 22 52 Malawi 4 220 36 Morocco 3 1250 72 Mozambique 4 90 44 Namibia 3 2220 42 Uganda 4 320 40 Azerbaijan 4 510 70 Bangladesh 4 270 59 Cambodia 4 52 52 Japan 2 3964 80 Lebanon 2 3350 70 Malaysia 3 1655 72 Nepal 4 33 55 Philippines 4 307 66 Saudi Arabia 2 6790 70 Turkey 2 3130 68 Turkmenistan 3 3047 66 Belarus 3 2150 68 Finland 2 5613 77 France 1 26050 78 Germany 1 28260 77 Lithuania 3 2291 71 Portugal 1 10450 75 Romania 3 1420 69 Ecuador 3 1590 69 El Salvador 3 1810 69 Mexico 2 3680 72 Puerto Rico 2 7010 74 Canada 1 19290 78
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