The following table gives results from two groups of students who took a nonproctored test. use a 0.01 significance level to test the claim that the samples are from populations with the same mean. group 1: n=30 mean=70.29 s=22.09 group 2: n=32 mean=74.26 s=18.15 original claim: opposite claim: alternative and null hypotheses: significance level: test statistic: p-value: reject or fail to reject: final conclusion:

Original claim is

Opposite claim is

Null and alternative hypotheses:

Significance level: 0.01

Test statistic:

We can use TI-84 calculator to find the test statistic and P-value. The steps are as follows:

Press STAT and the scroll right to TESTS

Scroll down to 2-SampTTest... and scroll to stats.

Enter below information.

Pooled: Yes

Calculate.

The output is in the attachment.

Therefore, the test statistic is:

P-value: 0.4412

Reject or fail to reject: Fail to reject

Final Conclusion: Since the p-value is greater than the significance level, we, therefore, fail to reject the null hypothesis and conclude that the there is sufficient evidence to support the claim that the samples are from populations with the same mean.

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! )

you would have to sell 24 bracelets.

step-by-step explanation:

84/3.5 = 24