Negative correlation - the coefficient is between -1 and 0Īn example of a negative correlation is shown below, with the accompanying Pearson's correlation coefficient (R).Positive correlation - the coefficient is between 0 and 1.No correlation - the coefficient is exactly 0.We can therefore distinguish between three basic types of correlation: A coefficient of zero signifies complete lack of a statistical association (orthogonality), while a coefficient of one (or minus one) suggests a perfect correlation (X and Y change in unison). A positive correlation coefficient reflects a straight relationship between the variables while a negative one reflects an inverse one (when X is higher, Y is lower, and vice versa). It quantifies both the strength and the direction of the relationship.
The dependence might be due to direct causality, indirect causality, or it might be entirely spurious.Ī correlation coefficient calculated for two variables, X and Y, is a measure of the extent to which the dependent variable (Y) tends to change with changes in the independent variable (X). We will observe that the two variables tend to change together, to an extent, suggesting some dependence between them. A trivial example would be to plot the change in average daily temperature and the consumption of ice cream, or the intensity of cloud coverage and rainfall precipitation in a given region. We say two random variables or bivariate data are correlated if there is some form of quantifiable association between them, some kind of statistical relationship.
The phenomenon measured by a correlation coefficient is that of statistical correlation.
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