Let X take on values −2, −1, 1, 2 each with probability 0.25. Let Y = X^2 , so that Y takes on values 1, 4 each with probability 0.5. Clearly, since Y is defined in terms of X, then Y is dependent on X. You can show quickly that Cov(X, Y ) = 0, and as a consequence, that ρ = 0. These random variables are uncorrelated, yet dependent.
Let X take on values −2, −1, 1, 2 each with probability 0.25. Let Y =...
Example of Covariance II 4 points possible (graded) Let X, Y be random variables such that • X takes the values +1 each with probability 0.5 . (Conditioned on X) Y is chosen uniformly from the set {-3X - 1,-3x, -3x+1}. (Round all answers to 2 decimal places.) What is Cov(x,x) (equivalent to Var (X))? Cov(X, X) = What is Cov(Y,Y) (equivalent to Var (Y))? Cov(Y,Y)= What is Cov(X,Y)? Cov(X,Y)= What is Cov(Y,X)? Cov(Y,X)= Submit You have used 0 of...
9. Let X and Y be two random variables. Suppose that σ = 4, and σ -9. If we know that the two random variables Z-2X?Y and W = X + Y are independent, find Cov(X, Y) and ρ(X,Y). 10. Let X and Y be bivariate normal random variables with parameters μェー0, σ, 1,Hy- 1, ơv = 2, and ρ = _ .5. Find P(X + 2Y < 3) . Find Cov(X-Y, X + 2Y) 11. Let X and Y...
1) Let X and Y be random variables. Show that Cov( X + Y, X-Y) Var(X)--Var(Y) without appealing to the general formulas for the covariance of the linear combinations of sets of random variables; use the basic identity Cov(Z1,22)-E[Z1Z2]- E[Z1 E[Z2, valid for any two random variables, and the properties of the expected value 2) Let X be the normal random variable with zero mean and standard deviation Let ?(t) be the distribution function of the standard normal random variable....
Please select 2 & 3 2. Let X and Y be discrete random variables taking values 0 or 1 only, and let pr(X = i, Y = j)-pij (jz 1,0;j = 1,0). Prove that X and Y are independent if and only if cov[X,Y) 0 3. If X is a random variable with a density function symmetric about zero and having zero mean, prove that cov[X, X2] 0.
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo 5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
1. Let X and Y have a discrete joint distribution with ( P(X = x, Y = y) = {1, 10, if (x, y) = (-1,1) if x = y = 0 elsewhere Show that X and Y are uncorrelated but not independent. [5 points] 2. Let X and Y have a discrete joint distribution with f(-1,0) = 0, f(-1,1) = 1/4, f(0,0) = 1/6, f(0, 1) = 0, $(1,0) = 1/12, f(1,1) = 1/2. Show that (a) the two...
The discrete random variables X and Y take integer values with joint probability distribution given by f (x,y) = a(y−x+1) 0 ≤ x ≤ y ≤ 2 or =0 otherwise, where a is a constant. 1 Tabulate the distribution and show that a = 0.1. 2 Find the marginal distributions of X and Y. 3 Calculate Cov(X,Y). 4 State, giving a reason, whether X and Y are independent. 5 Calculate E(Y|X = 1).
Let X1, X2, and X3 be uncorrelated random variables, each with 4. (10 points) Let Xi, X2, and X3 be uncorrelated random variables, each with mean u and variance o2. Find, in terms of u and o2 a) Cov(X+ 2X2, X7t 3X;) b) Cov(Xrt X2, Xi- X2)
2. Let X and Y be continuous random variables with joint probability density function fx,y(x,y) 0, otherwise (a) Compute the value of k that will make f(x, y) a legitimate joint probability density function. Use f(x.y) with that value of k as the joint probability density function of X, Y in parts (b),(c).(d),(e (b) Find the probability density functions of X and Y. (c) Find the expected values of X, Y and XY (d) Compute the covariance Cov(X,Y) of X...
3. Let the random variables X and Y have the joint probability density function 0 y 1, 0 x < y fxy(x, y)y otherwise (a) Compute the joint expectation E(XY) (b) Compute the marginal expectations E(X) and E (Y) (c) Compute the covariance Cov(X, Y)