Question

In: Statistics and Probability

Watch the TED talk by Hans Roling titled “The best stats you’ve ever seen”. You will need to include comments on this in your paper.

 

Steps (all statistical analysis to be done in Excel and/or StatCrunch):

Watch the TED talk by Hans Roling titled “The best stats you’ve ever seen”. You will need to include comments on this in your paper. Here is a link: http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen?language=en

Download the Excel data from IvyLearn

Create histograms of each of the variables (one histogram for fertility, one for life expectancy). Use the histograms to identify the shapes of the distribution. StatCrunch will be the easier tool to use for this particular task.

Calculate some descriptive statistics for each of the variables, including but not limited to the mean, median and standard deviation. Organize these numbers nicely in a table.

Using fertility as the predictor variable and life expectancy as the response variable, create a scatter diagram, come up with the least-squares regression line and calculate the linear correlation coefficient as well as the coefficient of determination. Make sure that you understand all interpretations and include them in your paper. Please carefully review the rubric below to see the full list of required interpretations.

Use the regression line to predict life expectancy for the United States given fertility and then compare this to the actual value in the United States.

Name some possible lurking variables that may be at work here.

Explain the difference between correlation and causation and why we cannot say that there is a cause and effect relationship in this situation.

Explain why we cannot use our regression model to predict the life expectancy of one particular individual.

Take a look at the website where this data was pulled from and comment on how the model might have been different if we used the data from 20, 40 or 60 years ago. Navigate to http://gapminder.org and click on “Gapminder World”. Use the x-axis and y-axis dropdown menus to ensure that ‘life expectancy (years)’ is selected on the y-axis and ‘children per woman (total fertility)’ is selected on the x-axis.

Country 2013 Fertility 2013 Life Expectancy
Afghanistan 4.9 56.2
Albania 1.771 75.8
Algeria 2.795 76.3
Angola 5.863 60.4
Antigua and Barbuda 2.089 75.2
Argentina 2.175 76
Armenia 1.74 73.8
Aruba 1.673 75.455
Australia 1.882 81.8
Austria 1.471 80.8
Azerbaijan 1.924 72.3
Bahamas 1.888 72.5
Bahrain 2.075 79
Bangladesh 2.177 69.5
Barbados 1.849 75.6
Belarus 1.494 70.2
Belgium 1.854 80.2
Belize 2.676 70
Benin 4.845 64.9
Bhutan 2.232 69.4
Bolivia 3.221 71.9
Bosnia and Herzegovina 1.283 77.5
Botswana 2.619 65.8
Brazil 1.801 75
Brunei 1.994 78.7
Bulgaria 1.541 74.5
Burkina Faso 5.605 62
Burundi 6.033 59.8
Cambodia 2.861 67.8
Cameroon 4.78 58.7
Canada 1.67 81.5
Cape Verde 2.292 74.2
Chad 6.263 57.1
Channel Islands 1.459 80.324
Chile 1.82 79.1
China 1.668 76.5
Colombia 2.286 75.6
Comoros 4.714 63.7
Congo, Dem. Rep. 5.933 57.5
Congo, Rep. 4.969 61.5
Costa Rica 1.795 79.8
Cote d'Ivoire 4.866 58.9
Croatia 1.501 77.8
Cuba 1.449 78.3
Cyprus 1.461 82.2
Czech Rep. 1.566 78.2
Denmark 1.88 79.9
Djibouti 3.387 63.4
Dominican Rep. 2.484 73.6
Ecuador 2.559 74.8
Egypt 2.77 70.9
El Salvador 2.184 73.9
Equatorial Guinea 4.845 58.8
Eritrea 4.696 62.1
Estonia 1.604 76.6
Ethiopia 4.519 62.6
Fiji 2.588 66.1
Finland 1.853 80.6
France 1.98 81.7
French Guiana 3.058 77.121
French Polynesia 2.058 76.257
Gabon 4.087 59.1
Gambia 5.751 64.3
Georgia 1.817 72.9
Germany 1.419 80.7
Ghana 3.857 64.9
Greece 1.529 79.8
Greenland 2.077 71.5
Grenada 2.17 71.5
Guadeloupe 2.08 80.947
Guam 2.405 78.854
Guatemala 3.783 72.3
Guinea 4.915 60.2
Guyana 2.546 64
Haiti 3.148 64.3
Honduras 3.001 72
Hong Kong, China 1.135 83.378
Hungary 1.411 75.8
Iceland 2.083 82.8
India 2.479 66.2
Indonesia 2.338 70.5
Iran 1.92 78.3
Iraq 4.026 71.3
Ireland 1.997 80.4
Israel 2.898 82.2
Italy 1.487 82.1
Jamaica 2.26 75.5
Japan 1.419 83.3
Jordan 3.244 78.1
Kazakhstan 2.455 67.8
Kenya 4.382 65.2
Kiribati 2.952 62
Korea, Dem. Rep. 1.988 71.2
Korea, Rep. 1.321 80.5
Kuwait 2.6 80.3
Kyrgyzstan 3.075 68.6
Laos 3.02 65.8
Latvia 1.607 75.3
Lebanon 1.495 78.3
Liberia 4.792 63.1
Libya 2.356 75.6
Lithuania 1.519 75
Luxembourg 1.671 81.1
Macao, China 1.083 80.4
Macedonia, FYR 1.431 76.6
Madagascar 4.468 64.3
Malawi 5.389 57.3
Malaysia 1.964 74.7
Maldives 2.256 79.3
Mali 6.847 57.2
Malta 1.356 82.1
Martinique 1.827 81.41
Mauritania 4.67 65.1
Mauritius 1.501 73.3
Mayotte 3.802 79.19
Mexico 2.185 75.5
Micronesia, Fed. Sts. 3.294 66.8
Moldova 1.456 71.9
Mongolia 2.436 64.7
Montenegro 1.666 75.6
Morocco 2.735 74.3
Mozambique 5.188 56.2
Myanmar 1.938 67.1
Namibia 3.051 60.6
Nepal 2.3 70.6
Netherlands 1.774 80.6
Netherlands Antilles 1.89 76.894
New Caledonia 2.127 76.306
New Zealand 2.052 80.6
Nicaragua 2.498 76.4
Niger 7.561 61.6
Nigeria 5.976 60.1
Norway 1.931 81.4
Oman 2.853 75.5
Pakistan 3.185 65.7
Panama 2.466 77.8
Papua New Guinea 3.781 59.8
Paraguay 2.864 73.7
Peru 2.417 77.1
Philippines 3.043 70
Poland 1.417 76.9
Portugal 1.315 79.8
Puerto Rico 1.636 78.864
Qatar 2.019 81.8
Reunion 2.232 79.646
Romania 1.417 76
Russia 1.595 71.3
Rwanda 4.508 65.3
Saint Lucia 1.912 74.5
Saint Vincent and the Grenadines 1.997 72.7
Samoa 4.147 71.8
Sao Tome and Principe 4.075 68.4
Saudi Arabia 2.644 77.9
Senegal 4.934 65.7
Serbia 1.365 77.7
Seychelles 2.18 73.3
Sierra Leone 4.705 57.7
Singapore 1.282 81.9
Slovak Republic 1.396 76.2
Slovenia 1.509 80
Solomon Islands 4.031 63.7
Somalia 6.563 57.7
South Africa 2.387 60.4
South Sudan 4.92 57.2
Spain 1.505 81.7
Sri Lanka 2.339 76.1
Sudan 4.42 68.9
Suriname 2.268 70.1
Sweden 1.928 81.8
Switzerland 1.533 82.7
Syria 2.964 72.4
Taiwan 1.065 79.3
Tajikistan 3.815 70.6
Tanzania 5.214 62.2
Thailand 1.399 74.9
Timor-Leste 5.855 71.4
Togo 4.639 63
Tonga 3.767 70.3
Trinidad and Tobago 1.797 71.2
Tunisia 2.008 77.1
Turkey 2.041 76.3
Turkmenistan 2.326 67.5
Uganda 5.867 59.8
Ukraine 1.47 71.7
United Arab Emirates 1.801 76.4
United Kingdom 1.892 81
United States 1.976 78.9
Uruguay 2.046 76.9
Uzbekistan 2.309 69.7
Vanuatu 3.382 64.6
Venezuela 2.39 75.4
Vietnam 1.743 76.3
Virgin Islands (U.S.) 2.487 80.152
West Bank and Gaza 4.01 74.6
Western Sahara 2.363 67.764
Yemen, Rep. 4.075 67
Zambia 5.687 56.7
Zimbabwe 3.486 56

Solutions

Expert Solution

Histogram of 2013 Fertility shows that the 2013 Fertility data is positively skewed distribution whereas Histogram of 2013 Life Expectancy shows that the 2013 Life Expectancy data is negatively skewed distribution.

From the scatter plot, it is observed that 2013 Life Expectancy (y) is negatively correlated with 2013 Fertility (x).

Pearson correlation of 2013 Fertility and 2013 Life Expectancy = -0.800

Since P-Value = 0.000<0.05, so correlation coefficient of x and y is significant.

Regression Analysis: 2013 Life Expectancy versus 2013 Fertility

The regression equation is
2013 Life Expectancy = 84.2 - 4.31x 2013 Fertility

Table 1
Predictor Coef SE Coef T P
Constant 84.1613 0.7176 117.28 0.000<0.05
2013 Fertility -4.3090 0.2307 -18.68 0.000<0.05

S = 4.51093 R-Sq = 64.0% R-Sq(adj) = 63.9%

Table 2
Analysis of Variance

Source DF SS MS F P
Regression 1 7101.7 7101.7 349.00 0.000
Residual Error 196 3988.3 20.3
Total 197 11090.0

From Table 1 we observed that Intercept (constant) and Slope (regression coefficient) are significantly present since p-values are less than 0.05.

Hence finally we get the regression equation:

2013 Life Expectancy = 84.2 - 4.31x 2013 Fertility

This regression equation is also significant (since from Table 2, we see that p-value <0.05).

coefficient of determination= 64.0% implies this regression eqution explains  64.0% of total variation in 2013 Life Expectancy.

From the above scatter plot with fitted line it is observed that the fitting is good.

Predicted life expectancy for the United States given fertility=84.2 - 4.31x1.976=75.683

The actual value of life expectancy in the United States=78.900

Residual=78.900-75.683=3.217


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