Correlation is the term used for the relation between two resp. among several features, states, functions or events where there is no causal interdependence. A correlation can have a result the fact that some correlators do not influence each other, others do influence each other strongly and even change each other. In case of some correlators, there is a randomly influenced relation.
The correlation measures first and foremost the power of a statistic relation, namely of two variables in relation to each other. This can lead to a positive or a negative correlation. A positive correlation exists when the result looks as follows:
“the more variable A… the more variable B”. This means therefore, that a high calculated value of one feature tendentially leads to a high calculated value for the second feature. A negative correlation exists when the result looks as follows: “the more variable A… the less variable B” or vice-versa. This means therefore, that a high calculated value of one feature tendentially leads to a low calculated value for the other feature. You can also calculate the strength of a statistic relation. Correlation measurement always takes place starting from the existing value pairs, the correlators. The process used to calculate the correlation is referred to as correlation analysis. This strength is expressed in a correlation coefficient. The strength is between -1 and +1.
Correlation is used in mathematical processes in the context of 2 statistical, quantitative variables and it has a certain importance in many practical fields, like biology, but also in industrial manufacturing processes where you consider interdependencies.