Covariance measures the relationship between two variables while correlation measures the strength and direction of that relationship. While covariance can range from negative infinity to positive infinity, correlation is always between -1 and 1.
Covariance is calculated by taking the average of the product of the deviations of two variables from their mean. Correlation is calculated by dividing the covariance by the product of the standard deviations of the two variables.
A positive covariance/correlation value indicates a direct relationship between the two variables, meaning that as one variable increases, the other variable also tends to increase. A negative covariance/correlation value indicates an inverse relationship, meaning that as one variable increases, the other variable tends to decrease.
Covariance and correlation can be used to understand the relationship between two variables and how they may affect each other. They can also be used to identify patterns and trends in data, as well as to make predictions and decisions based on the strength and direction of the relationship.
Covariance and correlation can only measure linear relationships between variables. They also do not imply causation, meaning that a strong correlation does not necessarily mean that one variable causes the other. Additionally, outliers or extreme values can heavily influence the results of covariance and correlation calculations.