Dependence is any statistical relationship between two random variables or two sets of data.
Correlation refers to any of a broad class of statistical relationships involving dependence.
Variance measures how far a set of numbers is spread out. (A variance of zero indicates that all the values are identical.)
A non-zero variance is always positive: A small variance indicates that the data points tend to be very close to the mean (expected value) and hence to each other, while a high variance indicates that the data points are very spread out from the mean and from each other.
The square root of variance is called the standard deviation. The variance is one of several descriptors of a probability distribution.
Covariance is a measure of how much two random variables change together.
If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the smaller values, i.e., the variables tend to show similar behavior, the covariance is positive.
In the opposite case, when the greater values of one variable mainly correspond to the smaller values of the other, i.e., the variables tend to show opposite behavior, the covariance is negative.
The sign of the covariance therefore shows the tendency in the linear relationship between the variables.
Reference:
www.wikipedia.org
Correlation refers to any of a broad class of statistical relationships involving dependence.
Variance measures how far a set of numbers is spread out. (A variance of zero indicates that all the values are identical.)
A non-zero variance is always positive: A small variance indicates that the data points tend to be very close to the mean (expected value) and hence to each other, while a high variance indicates that the data points are very spread out from the mean and from each other.
The square root of variance is called the standard deviation. The variance is one of several descriptors of a probability distribution.
Covariance is a measure of how much two random variables change together.
If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the smaller values, i.e., the variables tend to show similar behavior, the covariance is positive.
In the opposite case, when the greater values of one variable mainly correspond to the smaller values of the other, i.e., the variables tend to show opposite behavior, the covariance is negative.
The sign of the covariance therefore shows the tendency in the linear relationship between the variables.
Reference:
www.wikipedia.org
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