Answer:
Throughout the segment below, the definition including its particular question is mentioned.
Explanation:
The probability of developing cancer
= 0.07
The probability of someone not getting cancer
= 1 - 0.07
= 0.93
Provided that if women have cancer, the risk of someone not testing positive is:
= 0.10
Therefore, if a female requires cancer, the risk of testing positive
= 1 - 0.10
= 0.90
The Probability of positive test
= 0.055 (whenever a woman does not have cancer)
Therefore, whenever a woman does not have cancer, the risk of not testing positive will be:
= 1 - 0.05
= 0.955
Now,
By using the law of conditional probability, we get
⇒

⇒

⇒ P (having cancer as well as positive tests) = P(having cancer) × P(Effective results, because she has cancer)
⇒

⇒

Correspondingly,
P (not getting cancer and testing effective or positive)
=

=
P (with a good test result)
=

=
