This figure depicts how the covariance of X and Y might look in the case of positive covariance, negative covariance, and weak covariance:Ĭase 1 where (x,y) > 0: If (x,y) is greater than zero (i.e. As opposed to positive covariance, if the greater values of one variable (X) correspond to lesser values of another variable (Y) and vice-versa, then the variables are considered to have negative covariance. Negative covariance means both the variables (X, Y) move in the opposite direction. So, for example, if an increase in a person’s height corresponds with an increase in a person’s weight, there is positive covariance between the two. This tells you something about the linear relationship between the two variables. So, if greater values of one variable (X) seem to correspond with greater values of another variable (Y), then the variables are considered to have positive covariance. Positive covariance means both the variables (X, Y) move in the same direction (i.e. Depending on the diverse values, there are two main types: Positive and negative covariance. What are the different types of covariance?Ĭovariance can have both positive and negative values. y represents the mean (average) of the Y-variable.x represents the mean (average) of the X-variable.yi represents the values of the Y-variable.xi represents the values of the X-variable.For example, the covariance between two random variables X and Y can be computed using the following formula: The covariance formula calculates data points from their average value in a dataset. First, let’s look at how covariance is calculated in mathematical terms. We’ll explore the different types of covariance shortly. For example, if greater values of one variable tend to correspond with greater values of another variable, this suggests positive covariance. ![]() To simplify, covariance measures the joint variability of two random variables. What is covariance?Ĭovariance is a quantitative measure of the degree to which the deviation of one variable (X) from its mean is related to the deviation of another variable (Y) from its mean.
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