That is, if the values of one variable (more or smaller) correspond to the values of another variable, they are said to be in positive covariance. The variables, in this case, behave similarly. It is classified into two sorts based on this: Positive CovarianceĪ positive covariance between two variables indicates that they are heading in the same direction. ![]() Let Σ(X) and Σ(Y) be the expected values of the variables, the covariance formula can be represented as:Ĭovariance can be either positive or negative. Covariance is great for defining the type of relationship, but it's terrible for interpreting the magnitude. Positive covariance denotes a direct relationship and is represented by a positive number.Ī negative number, on the other hand, denotes negative covariance, which indicates an inverse relationship between the two variables. The greater this number, the more reliant the relationship. ![]() The covariance value can range from -∞ to +∞, with a negative value indicating a negative relationship and a positive value indicating a positive relationship. Covariance is a statistical term that refers to a systematic relationship between two random variables in which a change in the other reflects a change in one variable.
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