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Covariance measures the degree to which two variables change together. It indicates the direction of the linear relationship between two variables. If the covariance is positive, it means that as one variable increases, the other tends to increase as well. If the covariance is negative, it means that as one variable increases, the other tends to decrease. However, the magnitude of covariance doesn't tell us about the strength of the relationship.  

The covariance between two random variables X and Y is calculated using the following formula:

          Cov(X, Y) = E[(X - μX) * (Y - μY)] ----------------------------------------- [3465a]

Where:

• Cov(X, Y) is the covariance between X and Y.
• E[] denotes the expected value or mean.
• X and Y are the random variables.
• μX and μY are the means of X and Y, respectively.

Another way to express the covariance between two random variables can be given by,

 ----------------------------------------- [3465b]

where,

n−1 is the denominator in the formula and represents the degrees of freedom correction factor. In statistics, when calculating sample statistics (as opposed to population parameters), we use n−1 instead of n in the denominator to account for the fact that we're estimating parameters from sample data rather than from the entire population. This adjustment helps to produce unbiased estimates. 

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