The joint p.d.f. of the r.v.'s X and Y is given by fx,y (x, y) = (x + y²), 0 ≤ x ≤ 1, 0 ≤ y ≤ 1, 0, otherwise.

MATLAB: An Introduction with Applications
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Chapter1: Starting With Matlab
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The joint probability density function (p.d.f.) of the random variables (r.v.'s) \( X \) and \( Y \) is given by

\[
f_{X,Y}(x,y) = 
\begin{cases} 
\frac{6}{5}(x+y^2), & 0 \leq x \leq 1, \ 0 \leq y \leq 1, \\
0, & \text{otherwise.}
\end{cases}
\]
Transcribed Image Text:The joint probability density function (p.d.f.) of the random variables (r.v.'s) \( X \) and \( Y \) is given by \[ f_{X,Y}(x,y) = \begin{cases} \frac{6}{5}(x+y^2), & 0 \leq x \leq 1, \ 0 \leq y \leq 1, \\ 0, & \text{otherwise.} \end{cases} \]
(f) Evaluate \(E(X|Y = y)\) for any fixed \(y\) with \(0 \leq y \leq 1\).

(g) Evaluate \(E(E(X|Y))\) and compare it with \(E(X)\).

(h) Calculate the m.g.f. \(M_{X,Y}\).
Transcribed Image Text:(f) Evaluate \(E(X|Y = y)\) for any fixed \(y\) with \(0 \leq y \leq 1\). (g) Evaluate \(E(E(X|Y))\) and compare it with \(E(X)\). (h) Calculate the m.g.f. \(M_{X,Y}\).
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