Problem 2 Let the two random variables X and Y be independent, with the following Cumulative Distribution Functions (CDFS): x < -2 x<-2 1 (x+2) -2 0 Fx(x) = Fy(y) = 1 1 x >0 We want to: a) Compute and draw the two corresponding pdfs; b) Compute E[X], E[Y), var(X), var(Y); c) Compute the mean and variance of the r.v. Z = X+Y; d) Let V = 4X + 2; compute and draw the pdf of V; compute E[V] and var(V)
Problem 2 Let the two random variables X and Y be independent, with the following Cumulative Distribution Functions (CDFS): x < -2 x<-2 1 (x+2) -2 0 Fx(x) = Fy(y) = 1 1 x >0 We want to: a) Compute and draw the two corresponding pdfs; b) Compute E[X], E[Y), var(X), var(Y); c) Compute the mean and variance of the r.v. Z = X+Y; d) Let V = 4X + 2; compute and draw the pdf of V; compute E[V] and var(V)
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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![Problem 2
Let the two random variables X and Y be independent, with the following Cumulative
Distribution Functions (CDFS):
x < -2
x<-2
1
Fx(x) = (x + 2)2 -2 <x<0 ;
Fy(y) =
(x + 2) -2 < x<0
1
x >0
1
x >0
We want to:
a) Compute and draw the two corresponding pdfs;
b) Compute E[X], E[Y), var(X), var(Y);
c) Compute the mean and variance of the r.v. Z = X+Y;
d) Let V = 4X + 2; compute and draw the pdf of V; compute E[V] and var(V)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ffd54fbc2-091c-4efc-a0ae-33f00779b90d%2F50df7375-ff6d-4c56-b91d-74b2a5762a1c%2Fbkp8518_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Problem 2
Let the two random variables X and Y be independent, with the following Cumulative
Distribution Functions (CDFS):
x < -2
x<-2
1
Fx(x) = (x + 2)2 -2 <x<0 ;
Fy(y) =
(x + 2) -2 < x<0
1
x >0
1
x >0
We want to:
a) Compute and draw the two corresponding pdfs;
b) Compute E[X], E[Y), var(X), var(Y);
c) Compute the mean and variance of the r.v. Z = X+Y;
d) Let V = 4X + 2; compute and draw the pdf of V; compute E[V] and var(V)
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