Let X and Y be two independent Bernoulli( 1/2 ) random variables. Define random variables U and V by
U = X + Y and V = | (X - Y) | (abs. value)):
(a) Find the joint probability mass function of (U, V ).
Hints: note that U and V are taking integer values in {0, 1, 2} and {0, 1}, respectively.
(b) Determine the covariance Cov(U, V ):
(c) Find Var(U), Var(V ) and determine the correlation coeffcient p(U, V ):
Let X and Y be two independent Bernoulli( 1/2 ) random variables. Define random variables U...
Let X and Y be two independent Bernoulli (05) randon variables and define U = X + Y and (a) Find the joint and marginal probability mass functions for U and V. [It is sufficient to con struct a table to describe these mass functions.] (b) Are U and V independent? Why or why not? (c) Find the conditional probability mass functions pUv (u) and pv u(v). [Again, you can construct a table to describe these mass functions.]
Problem 3: 10 points σ2. Define Assume that U, V, and W are independent random variables with the same common variance X= + W and Y-V-W. 1. Find the variances Var[X] and Var[Y 2. Find the covariance between X and Y, that is: cov [x,Y 3. Find the covariance between (X+Y) and (X - Y), that is: COV[(X +Y), (X -Y)]
2. Let U and V be independent random variables, with P(U 1) 1/4 and P(U = -1) = P(V -1) 1) = P(V 3/4. Define X = U/V and Y = U V (a) Give the joint pmf of X and Y [4] (b) Calculate Cov(X,Y) [4]
2. Let U and V be independent random variables, with P(U 1) 1/4 and P(U = -1) = P(V -1) 1) = P(V 3/4. Define X = U/V and Y = U V...
Let X1 and X2 be independent random variables with means μ1 and μ2, and variances σ21 and σ22, respectively. Find the correlation of X1 and X1 + X2. Note that: The covariance of random variables X; Y is dened by Cov(X; Y ) = E[(X - E(X))(Y - E(Y ))]. The correlation of X; Y is dened by Corr(X; Y ) =Cov(X; Y ) / √ Var(X)Var(Y )
There are two independent Bernoulli random variables, U and V , both with probability of success 1/2. Let X=U+V and Y =|U−V|. 1) Calculate the covariance of X and Y 2) Explain whether X and Y are independent or not 3) Identify the random variable expressed as the conditional expectation of Y given X, i.e., E[Y |X].
Let X1 d= R(0,1) and X2 d= Bernoulli(1/3) be two independent random variables, define Y := X1 + X2 and U := X1X2. (a) Find the state space of Y and derive the cdf FY and pdf fY of Y . (You may wish to use {X2 = i}, i = 0,1, as a partition and apply the total probability formula.) (b) Compute the mean and variance of Y in two different ways, one is through the pdf of Y...
Let X and Y be two independent random variables such that E(X) = E(Y) = u but og and Oy are unequal. We define another random variable Z as the weighted average of the random variables X and Y, as Z = 0X + (1 - 0)Y where 0 is a scalar and 0 = 0 < 1. 1. Find the expected value of Z , E(Z), as a function of u . 2. Find in terms of Oy and...
5. (2 points) Let X and Y be Bernoulli random variables. Show that X and Y are independent if and only if Cov(X, Y) = 0.
9. Let X and Y be independent and identically distributed random variables with mean u and variance o. Find the following: (a) E[(x + 2)] (b) Var(3x + 4) (c) E[(X-Y)] (d) Cov{(X + Y), (X - Y)}
4.2 The Correlation Coefficient 1. Let the random variables X and Y have the joint PMF of the form x + y , x= 1,2, y = 1,2,3. p(x,y) = 21 They satisfy 11 12 Mx = 16 of = 12 of = 212 2 My = 27 Find the covariance Cov(X,Y) and the correlation coefficient p. Are X and Y independent or dependent?