Assume that $X_1$, and $X_2$ are i.i.d. normal random variables with mean $0$ and variance $1$. Let $Y_1$ and $Y_2$ be defined as $Y_1 =8X_1+6X_2$ and $Y_2 = X_1$.
- $E[Y_1]= 0$ correct? because it's just $8 \times 0 + 6 \times 0$?
- $Var(Y_1) = 100$? I'm getting this because $Var(x) = a^2Var(X)$ So each equal $64($variance of $X_1 = 1) + 36($variance of $X_2 = 1) = 100$.
- $P(Y_1 \ge 12)= 1 - P(Y_1 < 12) = $Do I use the standard normal table for this and shift the standard normal over or something like that?
- $Cov(Y_1,Y_2) =? $. I know $$Cov(X,Y) = E[(X-E[X])·(Y- E[Y])] = E[XY ]- E[X]E[Y ]$$ but I'm not sure how I apply it in this situation.
Any help solving these problems and offering suggestions would be greatly appreciated. Thank you all very much! I'm happy to also offer more clarification.
Correct.
Correct.
If you use tables for your normal CDF calculations then yes, recast to a standard normal with $Z=\frac{Y_1-\mu}{\sigma}$ and read off the number you need.
You're a fair bit of the way there. The next step is to write $E[Y_1 Y_2]$ in terms of expectations involving $X_1,X_2$, specifically: $E[Y_1 Y_2]=E[(8X_1+6X_2)X_1]=E[8X_1^2+6X_2X_1]$. Any ideas for the next step?