**Correlation** is a statistical measure of the extent to which two variables relate to one another. One commonly used measure of the linear correlation between two continuous variables is **Pearson’s correlation coefficient** (denoted by the symbol ρ for population or the letter r for a sample).

Given the values of two variables for a set of observations (X is usually used to denote the independent variable and Y for the dependent variable), Pearson’s correlation coefficient can be calculated using a mathematical formula. As a result of the formula used to compute the correlation coefficient, its value will always lie between -1 and 1.

If the Pearson’s correlation coefficient for a sample is positive (r > 0), then the independent variable (X) and the dependent variable (Y) are positively correlated. In other words, large values for X correspond to large values for Y, and vice versa. If r < 0 (negative correlation coefficient), then X and Y are negatively correlated. In other words, larger values for X correspond to smaller values for Y, and vice versa. If r = 0 then there is not a relationship among the variables. The strength of the correlation is indicated by how close r is to 1 or -1.

For example, if r= -0.8, X and Y have a strong negative correlation. However, if r= 0.3, the correlation is positive, but it is not a strong correlation.