COVARIANCE Meaning and
Definition
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Covariance is a statistical measure that quantifies the degree to which two variables tend to vary together. It is a measure of the joint variability of two random variables from their means. In other words, it determines the extent to which changes in one variable correspond to changes in another variable.
Covariance can take on positive, negative, or zero values. A positive covariance implies that the two variables tend to move in the same direction, meaning that as one variable increases, the other variable also tends to increase. On the other hand, a negative covariance indicates an inverse relationship, where as one variable increases, the other variable generally decreases.
Covariance is calculated by multiplying the difference between each pair of observations of the two variables by each other and then finding the average of these products. The resulting number represents the covariance between the variables. However, it is important to note that the magnitude of the covariance is not easily interpretable on its own, as it depends on the scales of the variables being measured.
Covariance is a crucial concept in statistics and data analysis as it helps determine the relationship between two variables. It is often used in various fields, including finance, economics, and social sciences, to understand and analyze the interdependencies between different variables in a dataset.
Common Misspellings for COVARIANCE
Etymology of COVARIANCE
The word "covariance" comes from the combination of two terms: "co-" meaning together or jointly, and "variance" which refers to the variability or dispersion of a random variable. The prefix "co-" in this context denotes the idea of shared or common variation between two variables. Therefore, "covariance" essentially denotes the measurement of how two variables vary or change together.
Similar spelling words for COVARIANCE
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