Given X = {0, 1} with X = 1 indicating disease. Given their status, a diagnostic test returns a classification of Y = {0, 1} with Y= 1 indicating a positive result: X~ Ber(p) Y | X~ Ber(px) Recall that the pmf of a generic Ber(π) random variable, W, is fw (w) = pw(1 − p)¹-w 0 < π≤ 1 for w = {0, 1} and fw (w) = 0 elsewhere. 1. Find the marginal expectation E[Y] in terms of p, p₁, and po. 2. Find the Cov(Y, X) in terms of p, p₁, and po. 3. Find an expression for P(X = 1 | Y = y) only in terms of y = p², and p. 4. Prove that the conditional variance of X is V[X | Y = y] = P(X = 1 | Y = y)(1 − P(X = 1 | Y = y))

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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Kindly answer question 3 and 4

Given X = {0, 1} with X = 1 indicating disease. Given their status, a diagnostic test returns a classification
of Y = {0, 1} with Y= 1 indicating a positive result:
X ~ Ber(p)
Y | X
Ber(px)
Recall that the pmf of a generic Ber(π) random variable, W, is
fw (w) = pw(1 − p)¹-w
-
0 = π = 1
for w = {0, 1} and fw (w) = 0 elsewhere.
1. Find the marginal expectation E[Y] in terms of p, p₁, and po.
2. Find the Cov(Y, X) in terms of p, p₁, and po.
3. Find an expression for P(X = 1 | Y = y) only in terms of y = P², and p.
4. Prove that the conditional variance of X is
V[X | Y = y] = P(X = 1 | Y = y)(1 − P(X = 1 | Y = y))
Transcribed Image Text:Given X = {0, 1} with X = 1 indicating disease. Given their status, a diagnostic test returns a classification of Y = {0, 1} with Y= 1 indicating a positive result: X ~ Ber(p) Y | X Ber(px) Recall that the pmf of a generic Ber(π) random variable, W, is fw (w) = pw(1 − p)¹-w - 0 = π = 1 for w = {0, 1} and fw (w) = 0 elsewhere. 1. Find the marginal expectation E[Y] in terms of p, p₁, and po. 2. Find the Cov(Y, X) in terms of p, p₁, and po. 3. Find an expression for P(X = 1 | Y = y) only in terms of y = P², and p. 4. Prove that the conditional variance of X is V[X | Y = y] = P(X = 1 | Y = y)(1 − P(X = 1 | Y = y))
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