Answer:
Part a: The probability that someone tests positive and does not have the disease is given as P(F'|E) is 0.6667
Part b: The probability that someone tests negative and have the disease is given as P(F|E') is 0.000000103
Step-by-step explanation:
The event that the test is positive is E and the patient has disease is F
so
The probability of a person having disease is P(F)=1/10000=0.0001
The probability of a person having disease and test positive is P(E|F)=0.999
The probability of a person not having disease and testing positive is P(E|F')=0.0002
Now by using complement rule, the value of people not having the disease is P(F')=1-P(F)=1-0.0001=0.9999
Part a:
The probability that someone tests positive and does not have the disease is given as P(F'|E) by Bayes theorem is as

By substitution of the values

The probability that someone tests positive and does not have the disease is given as P(F'|E) is 0.6667
Part b
The probability that someone tests positive and does not have the disease is given as P(F|E') by Bayes theorem is as

The probabilities are calculated as
P(E'|F)=1-P(E|F)=1-0.999=0.001
P(E'|F')=1-P(E|F')=1-0.0002=0.9998
By substitution of the values

The probability that someone tests negative and have the disease is given as P(F|E') is 0.000000103