Bivariate data in the context of "Level of measurement"

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.

Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation).

In statistics, correlation is a kind of statistical relationship between two random variables or bivariate data. Usually it refers to the degree to which a pair of variables are linearly related. In statistics, more general relationships between variables are called an association, the degree to which some of the variability of one variable can be accounted for by the other.

The presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation).Furthermore, the concept of correlation is not the same as dependence: if two variables are independent, then they are uncorrelated, but the opposite is not necessarily true: even if two variables are uncorrelated, they might be dependent on each other.