covariance and variance

All info are from wikipedia.

Covariance: 

In probability theory and statistics, covariance is a measure of how much two random variables change together. 
1)  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.  
2) 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.
3) The sign of the covariance therefore shows the tendency in the linear relationship between the variables. The magnitude of the covariance is not easy to interpret. The normalized version of the covariance, the correlation coefficient, however, shows by its magnitude the strength of the linear relation.




  
Variance: 
In probability theory and statistics, variance measures how far a set of numbers is spread out.
1) (A variance of zero indicates that all the values are identical.)
2) 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, which has the same unit as the variables. It measure the averaged difference between variable values and the mean variable value.  So standard deviation tells the mean difference between an individual value and the mean value.