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Correlation shows the direction (positive or negative) and strength (strong, weak or null) of relationship between two variables. It is represented using correlation coefficient. Its value lies between -1 to +1; higher the magnitude, stronger the relationship between variables. Types of correlation coefficient are as follows: There can be cases wherein the correlation coefficient is zero for a known cause and effect relationship - this is because of the following reasons: 1. Non linear relationship between variables One assumption while working with Correlation coefficient and regression is that there is a linear relationship between variables. If this assumption is violated, the correlation coefficient values can vary significantly. 2. Interaction effect Interaction between the variables in consideration and other external variables can cause the correlation coefficient to be zero. (Example) In ecommerce, customer satisfaction is poor for slow deliveries. As delivery time improves, the customer satisfaction improves significantly at first. Beyond a certain point any improvement in delivery time will not impact the customer satisfaction - satisfaction will be affected by other variables like quality, customer support, pricing etc. thereby dropping the coefficient value close to zero despite a known cause and effect relationship