# For any $c_1,c_2\in\mathbb{C}, E(c_1Z+c_2)=c_1E(Z)+c_2$

When dealing with real-valued RVs, the extensions of expectation and variance are quite clear to me. For example, showing

$E(aX+b)=aE(X)+b$ and $var(aX+b)=a^2var(X)$ is relatively straightforward to me. Given my weak background in complex numbers, the analogous situation in $\mathbb{C}$ is much more elusive to me.

So, how does one prove (rigorously) the two situations below:

$Z:=X+iY$

$\forall c_1,c_2\in\mathbb{C}, E(c_1Z+c_2)=c_1E(Z)+c_2$

$var(Z)=E(|Z|^2)-|E(Z)|^2=var(X)+var(Y)$

What is especially confusing (and simultaneously fascinating) is that this result apparently is unaffected by the correlation between $X$ and $Y$. How is this possible?

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If $E(\vert Z\vert)<\infty$ which is the case here since the mean and variance assumed to be well-defined, then the first relation is derived by just linearity of expectation $$E(c_1Z) = E(c_1 (X+ j Y)) = E(c_1X + jc_1 Y) = c_1 E(X) + j c_1 E(Y) = c_1 E(Z)$$ Note that $E(\vert X\vert)< E(\vert Z\vert)<\infty$ and the similar relation is also true for $Y$.
For the second one, let $m_z=E(Z)= m_x + j m_y$: $$var(Z) = E((Z-m_z)(Z-m_z)^*) = E((X-m_x + j(Y-m_y))(X-m_x + j (Y-m_y))^*)$$ Let $U$ be $X-m_x$ and $V$ be $Y-m_y$ the centralized random variables. Then, $$Var(Z) = E((U+jV)(U+jV)^*) = E(UU^* -jUV^* +jVU^* + VV^*) = E(U^2) + E(V^2) = var(U) + var(V)$$
The third equality is correct since $U$ and $Y$ are real-valued RVs.
where is $c_2$ addressed in the solution for part 1? – Justin Aug 11 '12 at 2:39