In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data.

- CorrelationThese inferences may take the form of answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation), and modeling relationships within the data (for example, using regression analysis).

- Statistics2 related topics

## Causality

Influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object ( an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.

Influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object ( an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.

Alternative methods of structure learning search through the many possible causal structures among the variables, and remove ones which are strongly incompatible with the observed correlations.

Statistics and economics usually employ pre-existing data or experimental data to infer causality by regression methods.

## Pearson correlation coefficient

In statistics, the Pearson correlation coefficient (PCC, pronounced ) ― also known as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC), the bivariate correlation, or colloquially simply as the correlation coefficient ― is a measure of linear correlation between two sets of data.