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[1] The joint probability mass function of two discrete random variables A and B is Pab(a,b) = {ca?b, Sca²b, a= -2,2 and b =

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Answer #1

Firstly, we find the value of c. It can be found by noting that the sum of the values of the joint probability mass function summed over the support of A and B must equal to 1. Thus, we get:

\begin{align*} & \ \ \ \ \sum_{a} \sum_{b} P_{A,B}(a,b) &&= 1 \\ &\Rightarrow P_{A,B}(-2,1) + P_{A,B}(-2,2) + P_{A,B}(2,1) + P_{A,B}(2,2) &&= 1 \\ &\Rightarrow c*(-2)^2*1 + c*(-2)^2*2 + c*2^2*1 + c*2^2*2 &&= 1 \\ &\Rightarrow 4c + 8c + 4c + 8c &&= 1 \\ &\Rightarrow 24c &&= 1 \\ &\Rightarrow c &&= \frac{1}{24} \end{align*}

Thus, the joint probability mass function of A and B is given by:

\begin{align*} P_{A,B}(-2,1) &= \frac{1}{24}*(-2)^2*1 &&= \frac{4}{24} &&&= \frac{1}{6} \\ P_{A,B}(-2,2) &= \frac{1}{24}*(-2)^2*2 &&= \frac{8}{24} &&&= \frac{2}{6} \\ P_{A,B}(2,1)&= \frac{1}{24}*2^2*1 &&= \frac{4}{24} &&&= \frac{1}{6} \\ P_{A,B}(2,2)&= \frac{1}{24}*2^2*2 &&= \frac{8}{24} &&&= \frac{2}{6} \end{align*}

Moreover, the marginal probability mass function of A is given by:

\begin{align*} P_A(-2) &= P_{A,B}(-2,1) + P_{A,B}(-2,2) &&= \frac{1}{6} + \frac{2}{6} &&&= \frac{1}{2} \\ P_A(2) &= P_{A,B}(2,1) + P_{A,B}(2,2) &&= \frac{1}{6} + \frac{2}{6} &&&= \frac{1}{2} \end{align*}

The marginal probability mass function of B is given by:

\begin{align*} P_B(1) &= P_{A,B}(-2,1) + P_{A,B}(2,1) &&= \frac{1}{6} + \frac{1}{6} &&&= \frac{1}{3} \\ P_B(2) &= P_{A,B}(-2,2) + P_{A,B}(2,2) &&= \frac{2}{6} + \frac{2}{6} &&&= \frac{2}{3} \end{align*}

(i)

To find whether A and B are uncorrelated, we find the covariance between A and B. For that, we first find E(AB), E(A) and E(B):

\begin{align*} E(AB) &= \sum_a \sum_b ab*P_{A,B}(a,b) \\ &= (-2)*1*P_{A,B}(-2,1) + (-2)*2*P_{A,B}(-2,2) \\ & \ \ \ + 2*1*P_{A,B}(2,1) + 2*2*P_{A,B}(2,2) \\ &= -2*\frac{1}{6} - 4*\frac{2}{6} + 2*\frac{1}{6} + 4*\frac{2}{6} \\ &= -\frac{2}{6} - \frac{8}{6} + \frac{2}{6} + \frac{8}{6} \\ &= 0 \end{align*}

\begin{align*} E(A) &= \sum_a a*P_A(a) \\ &= (-2)*P_A(-2) + 2*P_A(2) \\ &= -2*\frac{1}{2} + 2*\frac{1}{2} \\ &= -1 + 1 \\ &=0 \end{align*}

\begin{align*} E(B) &= \sum_b b*P_B(b) \\ &= 1*P_B(1) + 2*P_B(2) \\ &= 1*\frac{1}{3} + 2*\frac{2}{3} \\ &= \frac{1}{3} + \frac{4}{3} \\ &= \frac{5}{3} \end{align*}

Now, we find the covariance between A and B:

\begin{align*} Cov(A,B) &= E(AB) - E(A) E(B) \\ &= 0-0*\frac{5}{3} \\ &= 0-0 \\ &= 0 \end{align*}

Since, the covariance between A and B is zero, A and B are uncorrelated. [ANSWER]

(ii)

For A and B to be independent it must satisfy \begin{align*} P_{A,B}(a,b) = P_A(a)*P_B(b) \end{align*} for a = -2,2 and b = 1,2.

Now, we find:

\begin{align*} P_A(-2)*P_B(1) &= \frac{1}{2}*\frac{1}{3} &&= \frac{1}{6} &&= P_{A,B}(-2,1) \\ P_A(-2)*P_B(2) &= \frac{1}{2}*\frac{2}{3} &&= \frac{2}{6} &&= P_{A,B}(-2,2) \\ P_A(2)*P_B(1) &= \frac{1}{2}*\frac{1}{3} &&= \frac{1}{6} &&= P_{A,B}(2,1) \\ P_A(2)*P_B(2) &= \frac{1}{2}*\frac{2}{3} &&= \frac{2}{6} &&= P_{A,B}(2,2) \end{align*}

From above, we observe that \begin{align*} P_{A,B}(a,b) = P_A(a)*P_B(b) \end{align*} for a = -2,2 and b = 1,2. Thus, A and B are independent. [ANSWER]

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