Country ,Infant deaths/1000 ,health $ per capita ,Obesity %,Average Income,Suicides/ 100,000,Life expectancy,Universal Health Care?, % Diabetes,...

Country ,Infant deaths/1000 ,health $ per capita ,Obesity %,Average Income,Suicides/ 100,000,Life expectancy,Universal Health Care?, % Diabetes, Leading Cause of Death, Hospital beds/ 100,000
Algeria   21.9 362   23.6   4270   3.1   75   no   7.5   Stroke   17
Argentina   11.1   605   26.5   11960   14.2   76   no   6   Heart Disease   50
Australia   3   6031   29.9   54420   11.8   83   yes   5.1   Heart Disease   38
Austria   2.9   5581   20.1   45230   16.4   82   yes   6.9   Heart Disease   76
Belgium   3.3   4884   22.1   41860   20.5   81   yes   5.1   Heart Disease   62
Brazil   14.6   947   20.1   8840   6.3   75   no   10.4   Heart Disease   22
Burkina Faso   60.9   35   5.2   640   9.2   59   no   2.2   Influenza   4
Canada   4.3   5292   30.1   43660   12.3   82   yes   7.4   Heart Disease   27
China   9.2   420   7.3   8260   10   76   no   9.8   Stroke   42
Colombia   13.6   569   20.7   6320   6.1   74   no   10   Heart Disease   15
Denmark   2.9   6463   21   56730   12.2   81   yes   7.2   Heart Disease   31
Ecuador   18.4   579   18   5820   7.5   76   no   9.2   Heart Disease   15
Ethiopia   41.4   27   3.3   660   8.4   65   no   3.4   Influenza   2
Finland   1.9   4612   22.8   44730   16.2   81   no   6   Heart Disease   49
France   3.5   4959   25.7   38950   16.9   83   yes   5.3   Lung Cancer   65
Germany   3.1   5411   22.7   43660   13.4   81   yes   7.4   Heart Disease   83
Ghana   42.8   58   10.9   1380   6.9   62   yes   2.3   Stroke   9
Greece   3.6   1743   25.1   18960   4.3   82   yes   5.2   Heart Disease   48
Guatemala   24.3   233   16.4   3790   2.5   72   no   11.1   Influenza   6
Iceland   1.6   4662   23.9   56990   13.1   83   yes   6.1   Heart Disease   32
India   37.9   75   4.7   1680   15.7   68   no   9.3   Heart Disease   7
Ireland   3   4239   27   52560   11.7   82   yes   4.4   Heart Disease   28
Israel   3.2   2910   25.8   36190   5.5   82   yes   7.5   Heart Disease   31
Italy   2.9   3258   23.7   31590   7.9   84   yes   5.1   Heart Disease   34
Japan   2   3703   3.5   38000   19.6   84   yes   5.7   Stroke   134
Kenya   36.6   78   5.9   1380   6.5   62   no   2.4   Influenza   14
Lebanon   7.3   569   30.8   7680   3.1   80   no   13   Heart Disease   29
Luxembourg   1.6   8138   24.8   76660   11.1   82   yes   4.7   Heart Disease   49
Malta   5.2   2471   28.7   24140   6   82   yes   9.9   Heart Disease   47
Mexico   11.9   677   27.6   9040   5   77   no   15.8   Diabetes   16
Myanmar   40.7   20   2.9   1190   4.3   66   no   6.8   Stroke   9
Netherlands   4.8   5694   21.9   46310   12.6   82   yes   5.5   Lung Cancer   47
New Zealand   3.3   4896   30.6   39070   11.9   82   yes   7.3   Heart Disease   28
Nicaragua   19.4   178   15.5   2050   9.5   75   no   9.2   Heart Disease   9
Norway   2.2   9522   24.8   82330   10.9   82   yes   6   Heart Disease   39
Peru   13.6   359   20.4   5950   5.8   75   no   6.9   Influenza   16
Portugal   3   2097   22.1   19850   13.6   82   yes   9.9   Stroke   34
Spain   3.6   2658   26.5   27520   8.5   83   yes   7.7   Heart Disease   30
Sweden   2.4   6808   22   54630   15.4   83   yes   4.7   Heart Disease   26
Switzerland   3.5   9674   21   81240   15.1   83   yes   6.1   Heart Disease   47
Tunisia   12.1   785   27.1   3690   5.5   75   no   9.6   Heart Disease   22
Turkey   11.6   1037   29.4   11180   8.7   75   yes   12.8   Heart Disease   27
United Arab Emirates   5.9   2405   34.5   40480   2.9   78   yes   19.3   Heart Disease   12
United Kingdom   3.5   3377   29.8   42390   8.5   82   yes   4.7   Heart Disease   28
United States   5.6   9403   35   56180   14.3   79   no   10.8   Heart Disease   29
Venezuela, RB   12.9   923   24.3   12500   3   74   no   12   Heart Disease   8

Task 7: Best Way to Measure the Center (11 points)

The center of a data set is a value that represents a “typical” data point. There are three ways to measure the center of a data set: mean, median, or mode. In a perfect data set, the mean, median, and mode would all be equal and would accurately represent the center of data set. Unfortunately, most real-world data sets are not perfect. Depending on the characteristics of the variable, we may need to choose the best way represent the center, or “typical” value. For each variable in the Global Health Summary data set, you will need to investigate and decide what measure of center (mean, median, or mode) should be used to best represent a “typical” value. For each variable, briefly explain how you came to your conclusion.

Hint: You may want to consider the type of variable (qualitative or quantitative), shape of the distribution, and/or if there are any outliers.

Variable (from the data set)	Best Measure of Center (Mean, Median, or Mode)	Explanation
Country
Infant Mortality (per 1000 live births)
Health Expenditure ($) per capita
Obesity Rate
Average Income (per capita)
Suicide per 100,000
Life Expectancy
Universal Health Care?
Diabetes Rate (%)
Leading Cause of Death
Hospital Beds per 100,000

Expert Solution

orchestra answered 2 years ago

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