If cov(X,Y)=15, what is restrictions should we put standard deviation ...
Restrictions on Standard Deviation of X and Y given Covariance
When we have the covariance between two variables X and Y, we can determine some restrictions on their standard deviation. The covariance between X and Y is given by:
Cov(X,Y) = E[(X - E[X])(Y - E[Y])]
where E[X] and E[Y] are the expected values of X and Y, respectively.
Positive Covariance
If the covariance between X and Y is positive, i.e., Cov(X,Y) > 0, then:
- Both X and Y tend to increase or decrease together.
- The standard deviation of X and Y must be positive.
- The standard deviation of X and Y cannot be zero.
- The standard deviation of X and Y can be anything greater than zero.
Negative Covariance
If the covariance between X and Y is negative, i.e., Cov(X,Y) < 0,="" />
- X tends to increase when Y decreases and vice versa.
- The standard deviation of X and Y must be positive.
- The standard deviation of X and Y cannot be zero.
- The standard deviation of X and Y can be anything greater than zero.
Zero Covariance
If the covariance between X and Y is zero, i.e., Cov(X,Y) = 0, then:
- X and Y are independent of each other.
- The standard deviation of X and Y can be anything greater than or equal to zero.
- If the standard deviation of X or Y is zero, then the variable is constant and not informative.
Conclusion
In conclusion, the covariance between X and Y provides information about their linear relationship. The standard deviation of X and Y must be positive and cannot be zero. If the covariance is zero, then the variables are independent, and their standard deviations can be anything greater than or equal to zero.