**Correlation** is a broader term of statistical relationship between two or more observed data values or random variables. These are useful due to its quality of indicating a predicative relationship which can be explored and used in general practice. Correlation can suggest possible relationship whether it is casual or mechanistic. Usually correlation is used to refer a specific relationship between two or more random variables or mean values. Correlation uses various correlation coefficients to measure the degree of relationship. Major correlation coefficients are:

**Pearson correlation coefficient:** It is also known as Pearson product-moment correlation coefficient and is sensitive to a linear relationship between two variables while any one of them may be a nonlinear function. It is computed by dividing the covariance of two variables by the calculated value of their standard deviations.

Pearson correlation coefficient: It is also known as Pearson product-moment correlation coefficient and is sensitive to a linear relationship between two variables while any one of them may be a nonlinear function. It is computed by dividing the covariance of two variables by the calculated value of their standard deviations.

The Pearson coefficient is -1 in the case of perfect decreasing linear relationship (or anti-correlation) and becomes +1 if the case has a perfect positive linear relationship. While in the case of coefficient is zero it shows less of a relationship in the variables.

Rank correlation coefficients: It is quite different than Pearson correlation coefficient and measures a different type of association. It is used in the case of one variable increases and the other variable does not.

Distance correlation was introduced to complete the deficiency of Pearsonâ€™s correlation that it may be zero for dependent variables.

Brownian correlation or covariance: Like distance correlation it was also designed to fulfill the deficiency of Pearsonâ€™s coefficient.

Partial correlation: It is used to measure the strength of dependence between two variables.

Besides this, correlation has several more types of variation depending on their properties. Generally these are Association (statistics), Autocorrelation, Brownian covariance, Canonical correlation, Coefficient of determination, Concordance correlation coefficient, Cophenetic correlation, Correlation function, Cross-correlation, Currency correlation, Distance correlation, Ecological correlation, Fraction of variance unexplained, Genetic correlation, Goodman and Kruskal’s lambda, Illusory correlation, Interclass correlation, Intraclass correlation, Linear correlation (wikiversity), Modifiable a real unit problem, Multiple correlation, Point-biserial correlation coefficient, Statistical arbitrage, Sub independence.