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hw02
ChE 132C due
Jan.21
, 2011
1)
HIV test example revisited.
The test is positive for 99.99% of those who are infected.
The test is also positive for 0.02% of those who are not infected.
For a population where only
1/10000 people are infected, we showed that if a randomly selected person tests positive he has a
1/3 chance of being infected.
This seems remarkably small for such an accurate test, but recall
that false positives are more common than true positives.
a)
Repeat the analysis for a population where 1/100 are infected.
b)
Now return to the population where 1/10000 are infected.
Our patient who just
tested positive decides to take a second type of test.
Within groups where
everyone is infected the second test is independent of the first test.
The second
test is also independent of the first test outcome within groups where everyone is
known to be uninfected.
If our patient tests positive again in the second test, what
is the probability that he is infected?
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This note was uploaded on 03/29/2011 for the course CHE 132C taught by Professor Peters during the Spring '11 term at UCSB.
 Spring '11
 PETERS

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