Let X be a discrete random variable with the following PMF pX(k) = 0.2 for k = 0 0.2 for k = 1 0.3 for k = 2 0.3 for k = 3 0 otherwise (i) Let Y = X(X − 1)(X − 2), find pY (y). (ii) Find E[Y ] and V AR[Y ].
The random variable Y can take the following values:
0(0-1)(0-2)=0
1(1-1)(1-2)=0
2(2-1)(2-2)=0
3(3-1)(3-2)=6
Then
"P(Y = 0) = P(X = 0) + P(X = 1) + P(X = 2) = 0.2 + 0.2 + 0.3 = 0.7"
"P(Y = 6) = P(X = 3) = 0.3"
i) We have a distribution series of the random variable Y
"\\begin{matrix}\nY&0&6\\\\\nP&{0.7}&{0.3}\n\\end{matrix}"
ii) Let's find
"E[Y] = \\sum {{y_i}} {p_i} = 0 \\cdot 0.7 + 6 \\cdot 0.3 = 1.8"
"Var[Y] = E[{Y^2}] - {E^2}[Y] = 0 \\cdot 0.7 + 36 \\cdot 0.3 - {1.8^2} = {\\rm{7}}{\\rm{.56}}"
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