As a result, lots of erroneous analysis gets published, in virtually all fields. Often times, there is simply not enough data to adequately define a correlation. This allows one to make ridiculous predictions which, although they can be supported by the data, make no sense. Does the value of one variable depend on the other or are the variables random. Correlation then determines the probability that the two variables are randomly correlated.
Correlations can be strong or weak.
If two variables are correlated, it means that one variable can be written as a function of the other. A positive association between the x and y variable helps you to predict the value of the other. Furthermore, the dispersion in the X variable is the same as that in the Y variable. The amount of clustering, i.e. the strength of association, is summarised by the correlation coefficient. In general, we measure correlation by a parameter known as the correlation coefficient, r .