There appears to be a very strong negative linear relationship between the two variables.
One variable increases as the other decreases following a linear relationship over the domains of measurement. A correlation coefficient can say nothing about causality. It is possible that changes in the first variable causes changes in the second or the other way around. Or, it could be that neither of them cause the other, but both are caused by something else.
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The graph follows a very strong downward trend. Would have helped if you specified which correlation coefficient; there are different types.
The correlation coefficient is a measure of linear association between two (or more) variables. It does not measure non-linear relationships nor does it say anything about causality.
Pearson's Product Moment Correlation Coefficient indicates how strong the relationship between variables is. A PMCC of zero or very close would mean a very weak correlation. A PMCC of around 1 means a strong correlation.
(a) Correlation coefficient is the geometric mean between the regression coefficients. (b) If one of the regression coefficients is greater than unity, the other must be less than unity. (c) Arithmetic mean of the regression coefficients is greater than the correlation coefficient r, provided r > 0. (d) Regression coefficients are independent of the changes of origin but not of scale.
A negative correlation highlights the similarity of opposites. In a negative correlation, as the value of variable a increases, the value of variable b decreases. It can work the other way too, as the value of a decreases, the value of b increases. A negative correlation is also routinely referred to as an inverse. For instance, every time a person goes to jail, there is one less person in the work force. Therefore, incarceration rates, and work force numbers have an inverse relationship.