It means that the two variables are likely dependent. The higher the number of the positive correlation the stronger the connection.
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A positive correlation coefficient means that as the value of one variable increases, the value of the other variable increases; as one decreases the other decreases. A negative correlation coefficient indicates that as one variable increases, the other decreases, and vice-versa.
I believe you are asking how to identify a positive or negative correlation between two variables, for which you have data. I'll call these variables x and y. Of course, you can always calculate the correlation coefficient, but you can see the correlation from a graph. An x-y graph that shows a positive trend (slope positive) indicates a positive correlation. An x-y graph that shows a negative trend (slope negative) indicates a negative correlation.
correlation is drawn from all data points. if you look at the r^2 value and it's below 0.99 for example (should be higher in non research work (and in much research work) it indicates that 1 of your points may be an outlier. If you input all datapoints into excel, you may be able to see the point that's throwing it off. There are also statistical tests you can do to spot an outlier. In other words, correlation is not independent of an outlier. it will make the r^2 value worse. If the outlier is taken out, then the correlation could be deemed independent but only because you manipulated it and had taken the outlier out
Correlation.
The correlation showing the weakest relationship is -74.