Given that X is Bernoulli random variable with parameter 1/2
"X\\sim Bernoulli(\\frac{1}{2})" then "E(X)=p=\\frac{1}{2}" and "V(X)=pq=\\frac{1}{2}\\cdot\\frac{1}{2}=\\frac{1}{4}"
We know that "V(X)=E(X^2)-(E(X))^2"
than "\\frac{1}{4}=E(X^2)-(\\frac{1}{2})^2 \\implies E(X^2)=\\frac{1}{4}+\\frac{1}{4}=\\frac{1}{2}"
Y is a standard normal random variable that is
"Y\\sim N(0,1)\\implies E(Y)=0" and "V(Y)=1"
"V(Y)=E(Y^2)-(E(Y))^2 \\implies 1=E(Y^2)-0^2\\implies E(Y^2)=1"
X and Y are independent variables and given that "Z = X + Y" then
"E(Z)=E(X)+E(Y)=\\frac{1}{2}+0=\\frac{1}{2}"
"V(Z)=V(X)+V(Y)+2Cov(X,Y)"
Since X and Y are independent Cov(X, Y)=0 then
"V(Z)=\\frac{1}{4}+1=\\frac{5}{4}"
"W=X-Y\\ so\\ E(W)=E(X)-E(Y)=\\frac{1}{2}-0=\\frac{1}{2}"
"V(W)=V(X)+V(Y)+2Cov(X,Y)=\\frac{1}{4}+1=\\frac{5}{4}"
"Cov(Z,W)=E(ZW)-E(Z)E(W)=E((X+Y)(X-Y))-E(Z)E(W)"
"Cov(Z,W)=E(X^2-Y^2)-\\frac{1}{2}\\cdot\\frac{1}{2}=E(X^2)-E(Y^2)-\\frac{1}{4}=\\frac{1}{2}-1-\\frac{1}{4}=-\\frac{3}{4}"
"Corr(Z,W)=\\frac{Cov(Z,W)}{\\sqrt{V(Z)}\\sqrt{V(W)}}=\\frac{-\\frac{3}{4}}{\\sqrt{\\frac{5}{4}}\\sqrt{\\frac{5}{4}}}=-\\frac{3}{5}"
Answer: "\\frac{5}{4},\\frac{5}{4},-\\frac{3}{4}, -\\frac{3}{5}" .
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