The data in the table and chart above was extracted from the world bank public data.Correlation was calculated using "Pearson's correlation", Quote from Wikipedia:
Pearson product-moment correlation coefficient is a measure of the correlation (linear dependence) between two variables X and Y, giving a value between +1 and -1 inclusive. It is widely used in the sciences as a measure of the strength of linear dependence between two variables. The correlation coefficient ranges from -1 to 1. A value of 1 implies that a linear equation describes the relationship between X and Y perfectly, with all data points lying on a line for which Y increases as X increases. A value of -1 implies that all data points lie on a line for which Y decreases as X increases. A value of 0 implies that there is no linear correlation between the variables.
The table below is an†Interpretation of the degree of correlation between two variables:
|None||-0.09 to 0.0||0.0 to 0.09|
|Small||-0.3 to -0.1||0.1 to 0.3|
|Medium||-0.5 to -0.3||0.3 to 0.5|
|Strong||-1.0 to -0.5||0.5 to 1.0|
The calculated value of the correlation coefficient between the population in a country living on less than $2.00 a day and¬†Life Expectancy at Birth¬† is -0.83, indicating that poverty and life expectancy at birth are strongly negatively correlated. What does this mean?
- Poverty in a country was quantified as a number which represents the percentage of the population in a country living on less than $2.00 a day
- Life Expectancy at Birth represents the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
In other words the higher a poverty number is, the poorer a country is, and the higher the life expectancy number is, the longer the people are expected to live.
The result was a strong negative correlation, indicating that the people of a country of (higher percentage of ¬†poverty) are expected to live less than people of a country with (smaller percentage of poverty)