Question

In: Statistics and Probability

The joint density of Y1, Y2 is given by f(y) = k, −1 ≤ y1 ≤...

The joint density of Y1, Y2 is given by f(y) = k, −1 ≤ y1 ≤ 1, 0 ≤ y2 ≤ 1, y1 + y2 ≤ 1, y1 − y2 ≥ −1, 0, otherwise

a. Find the value of k that makes this a probability density function.

b. Find the probabilities P(Y2 ≤ 1/2) and P(Y1 ≥ −1/2, Y2 ≤ 1/2

c. Find the marginal distributions of Y1 and of Y2.

d. Determine if Y1 and Y2 are independent

e. Calculate E(Y1Y2).

Solutions

Expert Solution

Please give it a thumbs up.

Thanks


Related Solutions

Suppose that Y1 and Y2 are random variables with joint pdf given by f(y1,y2) = ky1y2...
Suppose that Y1 and Y2 are random variables with joint pdf given by f(y1,y2) = ky1y2 ; 0 < y1 <y2 <1, where k is a constant equal to 8. a) Find the conditional expected value and variance of Y1 given Y2=y2. b) Are Y1 and Y2 independent? Justify your answer. c) Find the covariance and correlation between Y1 and Y2. d) Find the expected value and variance of Y1+Y2.
Let Y1 and Y2 have joint pdf f(y1, y2) = (6(1−y2), if 0≤y1≤y2≤1 0, otherwise. a)...
Let Y1 and Y2 have joint pdf f(y1, y2) = (6(1−y2), if 0≤y1≤y2≤1 0, otherwise. a) Are Y1 and Y2 independent? Why? b) Find Cov(Y1, Y2). c) Find V(Y1−Y2). d) Find Var(Y1|Y2=y2).
Suppose that Y1 and Y2 are jointly distributed with joint pdf: f(y1,y2) ={cy2. for 0 ≤...
Suppose that Y1 and Y2 are jointly distributed with joint pdf: f(y1,y2) ={cy2. for 0 ≤ y1 ≤ y2 ≤ 1 0. otherwise} (1) Find c value. (2) Are Y1 and Y2 independent? Justify your answer. (3) ComputeCov(Y1,Y2). (4) Find the conditional density of Y1 given Y2=y2. (5) Using (d), find the conditional expectation E (Y1|Y2). (6) Suppose that X1=Y1+Y2, and X2= 2Y2. What is the joint density ofX1andX2?
Given the joint probability density function f(x ,y )=k (xy+ 1) for 0<x <1--and--0<y<1 , find...
Given the joint probability density function f(x ,y )=k (xy+ 1) for 0<x <1--and--0<y<1 , find the correlation--ROW p (X,Y) .
Suppose X and Y are random variables with joint density f(x, y) = c(x2y + y2),...
Suppose X and Y are random variables with joint density f(x, y) = c(x2y + y2), − 1 ≤ x ≤ 1, 0 ≤ y ≤ 1 (0 else). a) Find c. b) Determine whether X and Y are independent. c) Compute P(3X + 2Y > 1 | −1/2 ≤ X ≤ 1/2).
Suppose that the joint probability density function of ˜ (X, Y) is given by:´ f X,Y...
Suppose that the joint probability density function of ˜ (X, Y) is given by:´ f X,Y (x,y) = 4x/y3 I(0.1)(x), I (1, ∞)(y). Calculate a) P(1/2 < X < 3/4, 0 < Y ≤ 1/3). b) P(Y > 5). c) P(Y > X).
Let X and Y have the following joint density function f(x,y)=k(1-y) , 0≤x≤y≤1. Find the value...
Let X and Y have the following joint density function f(x,y)=k(1-y) , 0≤x≤y≤1. Find the value of k that makes this a probability density function. Compute the probability that P(X≤3/4, Y≥1/2). Find E(X). Find E(X|Y=y).
5. Let Y1, Y2, ...Yn i.i.d. ∼ f(y; α) = 1/6( α^8 y^3) · e ^(−α...
5. Let Y1, Y2, ...Yn i.i.d. ∼ f(y; α) = 1/6( α^8 y^3) · e ^(−α 2y) , 0 ≤ y < ∞, 0 < α < ∞. (a) (8 points) Find an expression for the Method of Moments estimator of α, ˜α. Show all work. (b) (8 points) Find an expression for the Maximum Likelihood estimator for α, ˆα. Show all work.
1. Let ρ: R2 ×R2 →R be given by ρ((x1,y1),(x2,y2)) = |x1 −x2|+|y1 −y2|. (a) Prove...
1. Let ρ: R2 ×R2 →R be given by ρ((x1,y1),(x2,y2)) = |x1 −x2|+|y1 −y2|. (a) Prove that (R2,ρ) is a metric space. (b) In (R2,ρ), sketch the open ball with center (0,0) and radius 1. 2. Let {xn} be a sequence in a metric space (X,ρ). Prove that if xn → a and xn → b for some a,b ∈ X, then a = b. 3. (Optional) Let (C[a,b],ρ) be the metric space discussed in example 10.6 on page 344...
Consider a continuous random vector (Y, X) with joint probability density function f(x, y) = 1...
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]
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT