The probability that a given urine sample analyzed in a pathology lab contains protein is 0.73 (event a). the probability that the urine sample contains the protein albumin is 0.58 (event b).
event a: the sample contains protein. event b: the sample contains the protein albumin.
the probability that a sample contains protein and that the protein is albumin is 0.58. which statement is true?
a. events a and b are dependent because p(a and b) p(a) + p(b).
b. events a and b are dependent because p(a and b) = p(a) + p(b).
c. events a and b are dependent because p(a and b) = p(a)p(b).
d. events a and b are dependent because p(a and b) p(a) p(b).

idk

step-by-step explanation:

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! )