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1. X and Y are independent if fx\y(¤ | y) = fy(y). 2. min(X, Y) are considered as function of random variables X and Y. 3. E(X + Y) = E(X)+ E(Y) only if X and Y are independent.

1. X and Y are independent if fx\y(¤ | y) = fy(y). 2. min(X, Y) are considered as function of random variables X and Y. 3. E(X + Y) = E(X)+ E(Y) only if X and Y are independent.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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1. X and Y are independent if fx\y(¤ | y) = fy(y). 2. min(X, Y) are considered as function of random variables X and Y. 3. E(X + Y) = E(X)+ E(Y) only if X and Y are independent. 4. In general, E(XY)= E(X)E(Y). 5. Variance is always positive. 6. V(м) — 0 7. Covariance can be negative, zero or positive. 8. If X and Y are independent, then it follows that Cov(X, Y) = 0. 9. If Cov(X,Y) = 0, then X and Y are independent. 10. If Covariance is zero, then the correlation coefficient is 0. 11. If Y = 2X, then X and Y are not independent. 12. If Y — -2х, then p(X, Y) — 1. 13. If X and Y are independent, then p(X, Y) = 0. 14. The correlation coefficient ranges from 0 to 1.
Transcribed Image Text:1. X and Y are independent if fx\y(¤ | y) = fy(y). 2. min(X, Y) are considered as function of random variables X and Y. 3. E(X + Y) = E(X)+ E(Y) only if X and Y are independent. 4. In general, E(XY)= E(X)E(Y). 5. Variance is always positive. 6. V(м) — 0 7. Covariance can be negative, zero or positive. 8. If X and Y are independent, then it follows that Cov(X, Y) = 0. 9. If Cov(X,Y) = 0, then X and Y are independent. 10. If Covariance is zero, then the correlation coefficient is 0. 11. If Y = 2X, then X and Y are not independent. 12. If Y — -2х, then p(X, Y) — 1. 13. If X and Y are independent, then p(X, Y) = 0. 14. The correlation coefficient ranges from 0 to 1.
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