American Diabetes Association estimates that 8.3% of people in the United States have diabetes. Suppose that a medical lab has developed a simple diagnostic test for diabetes that is 98% accurate for people who have the disease and 95% accurate for people who do not have it. The medical lab gives the test to a randomly selected person. What is the probability that a person has diabeties given that the test says they do?

Probability that a person has diabeties given that the test says they do = 0.6526.

Step-by-step explanation:

We are give that an American Diabetes Association estimates that 8.3% of people in the United States actually have diabetes.

For testing this a simple diagnostic test for diabetes has been developed which is 98% accurate for people who have the disease and 95% accurate for people who do not have it.

Let A = Probability that people in United States actually have diabetes,

   P(A) = 8.3% or 0.083

P(A') = 1 - P(A) = means Probability that people in United States does not have diabetes = 1 - 0.083 = 0.917 .

Let B = Test that is accurate in detecting diabetes

P(B/A) = Probability that test says that person has diabetes given he/she actually has = 0.98

P(B/A') = Probability that test is accurate in detecting diabetes for people who actually does not have diabetes = 0.95

Probability that test says person has diabetes but in actually they don't have = 1 - 0.95 = 0.05.

Probability that test says person has diabetes = 0.083*0.98 + 0.05*(1-0.083)

                                                                             = 0.12719

Now, Probability that a person has diabetes given that the test says they do = Probability that person has diabetes ÷ Probability that test says they have

=  0.083 ÷ 0.12719 = 0.6526.

yes because a quadrilateral is equal to 4 sides and a rhombus has 4 sides so technacly it is a quadrilateral.

the answer   is 200

step-by-step explanation:

this is a problem about poisson probabilities.   i don't know if you've

already studied poisson distributions, or if you have a place to look

to read more about the subject.  

here's how you can see a poisson distribution coming.   when there are

a lot of events that may happen at any time (or any place), and when

you're counting how many of them happen at a particular time (or a

particular place), and each event is independent of the others, then

the result is a poisson distribution.   this is roughly true for the

chocolate chips; one chip being in a cookie has only a small effect on

the probability that another chip will be in the cookie.

once you know you have a poisson distribution, you know a lot.   there

is only one parameter to a poisson distribution, so if you know the

mean, you know everything.   the form of the distribution is

  probability of n chips = {[e^(-x)]*(x^n)} / (n! )