Answer:
39.17% probability that a woman in her 60s who has a positive test actually has breast cancer
Step-by-step explanation:
Bayes Theorem:
Two events, A and B.
In which P(B|A) is the probability of B happening when A has happened and P(A|B) is the probability of A happening when B has happened.
In this question:
Event A: Positive test.
Event B: Having breast cancer.
3.65% of women in their 60s get breast cancer
This means that 
A mammogram can typically identify correctly 85% of cancer cases
This means that 
Probability of a positive test.
85% of 3.65% and 100-95 = 5% of 100-3.65 = 96.35%. So
What is the probability that a woman in her 60s who has a positive test actually has breast cancer?
39.17% probability that a woman in her 60s who has a positive test actually has breast cancer
It’s not a good idea to beg
Given:
The growth of a sample of bacteria can be modeled by the function
where, b is the number of bacteria and t is time in hours.
To find:
The number of total bacteria after 3 hours.
Solution:
We have,
where, b is the number of bacteria and t is time in hours.
Substituting t=3, we get the number of total bacteria after 3 hours.
Number of bacteria cannot be decimal value. So, approximate the value to the nearest whole number.
Therefore, the number of total bacteria after 3 hours is about 3003.
<span>The answer is: 2. 11
Crowbar is a Class A lever
Therefore the mechanical advantage of the crowbar can be given by either:
MA = Output distance/Input distance
OR
MA = Output force/Input force
Since, the question gives only the force, we can use the second formula.
MA = Output force/ Input Force
= 330 N / 30 N
= 11</span>
