Mathematics, 28.12.2019 02:31 Aliyahh5988

One person wants to get a 95% z-confidence interval with a margin of error of at most 15 based on a population standard deviation of 60. what is the minimum sample size needed?

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Answer from: sherlock19

$n=(\frac{1.96(60)}{15})^2 =61.46 \approx 62$

So the answer for this case would be n=62 rounded up to the nearest integer

Step-by-step explanation:

Previous concepts

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".

The margin of error is the range of values below and above the sample statistic in a confidence interval.

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

$\bar X$ represent the sample mean for the sample

$\mu$ population mean

$\sigma=60$ represent the population standard deviation

n represent the sample size (variable of interest)

Confidence =95% or 0.95

The margin of error is given by this formula:

$ME=z_{\alpha/2}\frac{\sigma}{\sqrt{n}}$ (1)

And on this case we have that ME =15, and we are interested in order to find the value of n, if we solve n from equation (1) we got:

$n=(\frac{z_{\alpha/2} \sigma}{ME})^2$ (2)

The critical value for 95% of confidence interval is provided, $z_{\alpha/2}=1.96$ , replacing into formula (2) we got:

$n=(\frac{1.96(60)}{15})^2 =61.46 \approx 62$

So the answer for this case would be n=62 rounded up to the nearest integer

Answer from: Quest

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! )

Answer from: Quest

1) 2/5

2) zero=0/1

3) zero=0/2

4) -4/5

5) undefined=3/0

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One person wants to get a 95% z-confidence interval with a margin of error of at most 15 based on a...