suresh In statistics, covariance and correlation are both measures of the relationship between two variables.
Covariance is a measure of how two variables change together. It indicates the direction of the linear relationship between the variables. A positive covariance indicates that the variables tend to increase or decrease together, while a negative covariance indicates that one variable tends to increase as the other decreases. However, covariance does not have a standardized scale and can be difficult to interpret.
On the other hand, correlation is a standardized measure of the strength and direction of the linear relationship between two variables. Unlike covariance, correlation ranges from -1 to 1, where a correlation of 1 indicates a perfect positive relationship, a correlation of -1 indicates a perfect negative relationship, and a correlation of 0 indicates no relationship between the variables.
In summary, covariance measures the extent to which two variables change together, while correlation measures the strength and direction of that relationship in a standardized manner.
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