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Larry Greenbean
The correlation of the six different variables among showed many interesting inferences. If correlation between to variables shows that it is equal to one, then the behavior would be graphed in an ascending and to the right slope. If the conclusion were negative one then the slope would be tight, descending to the right. The closer the conclusion is to 1 or negative 1, the tighter the grouping in the graph. Likewise the further the conclusion is from a whole number and closer to zero, then the more sporadic it is. With that known here is what is found between different variables.

GNP per capita and Life Expectancy: This correlation is the 0.7 range meaning that it is positive and ascending to the right with a pretty good grouping. This shows that there is a distinct relation between the two variables.

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GNP and Adult Literacy: This is in the 0.6 range and shows positive and ascending correlation and is again showing a positive relation between the two variables.

GNP and GINI index of inequality: Interestingly this correlation is negative and would be grouped in a descending manner yet pretty tightly grouped. It is also interesting to point out that every correlation with GINI index is negative.

GNP and Public Expenditure on health: This correlation is high and 0.8 which shows a high cause and effect relation between the two variables.

GNP and Public Expenditure on Education: Interestingly these variables provide a low correlation of 0.4, which means there is a low connection between the two.

Life expectancy and Adult Literacy: This is an interesting correlation because it is so high with .76 meaning how long you live has a lot to do with how well you read or how well you read has a lot to do with how long you live.

Life expectancy and Public Expenditure on Health: It is .65, which is not as high as one would expect.
Generally what can be learned is that rising income and human welfare are very much in connection with one another. The income provides health care, which provides life expectancy, which relates to literacy. It is all very interesting. The largest exceptions to the data would have to those countries who have a large central government intervention as to the means of national income.

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