Respuesta :
Answer:
(a) The proportion of women who are tested, get a negative test result is 0.82.
(b) The proportion of women who get a positive test result are actually carrying a fetus with a chromosome abnormality is 0.20.
Step-by-step explanation:
The Bayes' theorem states that the conditional probability of an event E[tex]_{i}[/tex], of the sample space S, given that another event A has already occurred is:
[tex]P(E_{i}|A)=\frac{P(A|E_{i})P(E_{i})}{\sum\liits^{n}_{i=1}{P(A|E_{i})P(E_{i})}}[/tex]
The law of total probability states that, if events E₁, E₂, E₃... are parts of a sample space then for any event A,
[tex]P(A)=\sum\limits^{n}_{i=1}{P(A|B_{i})P(B_{i})}[/tex]
Denote the events as follows:
X = fetus have a chromosome abnormality.
Y = the test is positive
The information provided is:
[tex]P(X)=0.04\\P(Y|X)=0.90\\P(Y^{c}|X^{c})=0.85[/tex]
Using the above the probabilities compute the remaining values as follows:
[tex]P(X^{c})=1-P(X)=1-0.04=0.96[/tex]
[tex]P(Y^{c}|X)=1-P(Y|X)=1-0.90=0.10[/tex]
[tex]P(Y|X^{c})=1-P(Y^{c}|X^{c})=1-0.85=0.15[/tex]
(a)
Compute the probability of women who are tested negative as follows:
Use the law of total probability:
[tex]P(Y^{c})=P(Y^{c}|X)P(X)+P(Y^{c}|X^{c})P(X^{c})[/tex]
[tex]=(0.10\times 0.04)+(0.85\times 0.96)\\=0.004+0.816\\=0.82[/tex]
Thus, the proportion of women who are tested, get a negative test result is 0.82.
(b)
Compute the value of P (X|Y) as follows:
Use the Bayes' theorem:
[tex]P(X|Y)=\frac{P(Y|X)P(X)}{P(Y|X)P(X)+P(Y|X^{c})P(X^{c})}[/tex]
[tex]=\frac{(0.90\times 0.04)}{(0.90\times 0.04)+(0.15\times 0.96)}[/tex]
[tex]=0.20[/tex]
Thus, the proportion of women who get a positive test result are actually carrying a fetus with a chromosome abnormality is 0.20.