Suppose that X and Y have the following joint probability
density function. f (x, y) = (3/394)*y, 0 < x < 8, y > 0,
x − 3 < y < x + 3
(a) Find E(XY). (b) Find the covariance
between X and Y.
. The joint probability density function of X and Y is given
by
?(?, ?) = { ??^2? ?? 0 ≤ ? ≤ 2, 0 ≤ ?, ??? ? + ? ≤ 1
0 ??ℎ??????
(a) Determine the value of c.
(b) Find the marginal probability density function of X and
Y.
(c) Compute ???(?, ?).
(d) Compute ???(?^2 + ?).
(e) Determine if X and Y are independent
If the joint probability density function of the random
variables X and Y is given by f(x, y) = (1/4)(x + 2y) for 0 < x
< 2, 0 < y < 1, 0 elsewhere
(a) Find the conditional density of Y given X = x, and use it to
evaluate P (X + Y/2 ≥ 1 | X = 1/2)
(b) Find the conditional mean and the conditional variance of Y
given X = 1/2
(c) Find the variance...
Suppose X and Y have joint probability density function f(x,y) =
6(x-y) when 0<y<x<1 and f(x,y) = 0 otherwise.
(a) Indicate with a sketch the sample space in the x-y plane
(b) Find the marginal density of X, fX(x)
(c) Show that fX(x) is properly normalized, i.e., that it
integrates to 1 on the sample space of X
(d) Find the marginal density of Y, fY(y)
(e) Show that fY(y) is properly normalized, i.e., that it
integrates to 1 on...
Suppose that we have two random variables (Y,X) with joint
probability density function f(y,x). Consider the following
estimator of the intercept of the Best Linear Predictor:
A = ?̅ - B • ?̅ ,
where ?̅ is the sample mean of y, ?̅ is the sample mean of x, and B
is the sample covariance of Y and X divided by the sample variance
of X.
Identify the probability limit of A (if any). For each step in
your derivation,...
Consider a continuous random vector (Y, X) with joint
probability density function
f(x, y) = 1
for 0 < x < 1, x < y < x + 1.
What is the marginal density of X and Y? Use this to compute
Var(X) and Var(Y)
Compute the expectation E[XY]
Use the previous results to compute the correlation Corr (Y,
X)
Compute the third moment of Y, i.e., E[Y3]
Consider a continuous random vector (Y, X) with joint
probability density function f(x, y) = 1 for 0 < x < 1, x
< y < x + 1.
A. What is the marginal density of X and Y ? Use this to compute
Var(X) and Var(Y).
B. Compute the expectation E[XY]
C. Use the previous results to compute the correlation Corr(Y,
X).
D. Compute the third moment of Y , i.e., E[Y3].
The joint probability density function for two random variables
X and Y is given as, fx,y (x, y) = (2/3)(1 + 2xy3 ), 0
< x < 1, 0 < y < 1
(a) Find the marginal probability density functions for X and Y
.
(b) Are X and Y independent? Justify your answer.
(c) Show that E[X] = 4/9 and E[Y ] = 7/15 .
(d) Calculate Cov(X, Y )
The joint probability density function (PDF) of two random
variables (X,Y) is given by
???(?,?) = { 1, 0 ≤ ? ≤ 2,0 ≤ ? ≤ 1,2? ≤ ? 0, otherwise
1) Find the correlation coefficient ??? between the two random
variables X and Y
Find the probability P(Y>X/2).
help please asap