Often used in comparing bivariate data. Ex job satisfaction stratified by income.
The correlation coefficient varies between -1 and +1. Values approaching -1 or +1 indicate strong correlation (negative or positive) and values close to 0 indicate little or no correlation between x and y.
Correlation does not mean causation.
A positive correlation can be either good news or bad news
A negative correlation is not necessarily bad news. It merely means that as the independent variable goes more negative, the dependent variable goes negative as well.
r = 0; does not indicate the absence of a relationship, a curvilinear pattern may exist; r=-0.76 has the same predictive power as r = +0.76
Related to linear regression plots.
The pattern of dots is tighter with a strong correlation.