This means that the data points lie perfectly on a line with negative slope.
For example, the points (0,4), (1,3), (2,2), (4,0) are perfectly correlated since they lie on the line y = -x + 4.
It is a negative correlation since the slope of the line is -1, a negative number, or alternatively because as x rises, y falls.
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Negative
If the two variables increase together and decrease together AND in a linear fashion, the correlation is positive. If one increases when the other decreases, again, in a linear fashion, the correlation is negative.
Since y=14x is a perfect linear relation, the correlation would be 1.
If the data have a positive or negative correlation, it means the data have a linear relationship in the form of an equation of a line; or Y = mX + b.
It is a measure of the extent to which a linear change in one quantity is accompanied by a linear change in the other quantity. Note that only linear changes are measured and that there is no causality.