Average yearly temperature and GDP per capita show medium correlation levels

Correlation coefficient =-0.31

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 anInterpretation of the degree of correlation between two variables:

Correlation Negative Positive
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 correlation coefficient between the Average yearly temperature (1961-1990, Celsius) and GDP per capita, PPP (constant 2005 international $) equals to -0.31 indicating a medium negative correlation between the two indicators. In other words, this means that countries having a higher average yearly temperatures tend to have lower GDP per Capita.

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