Your friend tells you that the proportion of active Major League Baseball players who have a batting average greater than .300 is different from 0.51, a claim you would like to test. If you conduct a hypothesis test, what will the null and alternative hypotheses be

Null hypothesis: The proportion of active Major League Baseball players who have a batting average greater than 0.300 is 0.51 (p equals 0.51).

Alternate hypothesis: The proportion of active Major League Baseball players who have a batting average greater than 0.300 is not 0.51 (p not equals 0.51).

Step-by-step explanation:

A null hypothesis is a statement from a population parameter which is either rejected or accepted (fail to reject) upon testing. It is expressed using the equality sign.

An alternate hypothesis is also a statement from the population parameter which negates the null hypothesis and is accepted if the null hypothesis is rejected. It is expressed using any of the inequality signs.

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! )