Answer:
a)
And we can find this probability with this difference and with the normal standard table or excel:
b) ![P(0.11 < \bar X < 0.13)](https://tex.z-dn.net/?f=%20P%280.11%20%3C%20%5Cbar%20X%20%3C%200.13%29)
And we can use the z score defined by:
![z = \frac{\bar X -\mu}{\frac{\sigma}{\sqrt{n}}}](https://tex.z-dn.net/?f=%20z%20%3D%20%5Cfrac%7B%5Cbar%20X%20-%5Cmu%7D%7B%5Cfrac%7B%5Csigma%7D%7B%5Csqrt%7Bn%7D%7D%7D)
And using the limits we got:
![z = \frac{0.11-0.12}{\frac{0.009}{\sqrt{4}}}= -2.22](https://tex.z-dn.net/?f=%20z%20%3D%20%5Cfrac%7B0.11-0.12%7D%7B%5Cfrac%7B0.009%7D%7B%5Csqrt%7B4%7D%7D%7D%3D%20-2.22)
![z = \frac{0.13-0.12}{\frac{0.009}{\sqrt{4}}}= 2.22](https://tex.z-dn.net/?f=%20z%20%3D%20%5Cfrac%7B0.13-0.12%7D%7B%5Cfrac%7B0.009%7D%7B%5Csqrt%7B4%7D%7D%7D%3D%202.22)
And we want to find this probability:
![P(-2.22< Z](https://tex.z-dn.net/?f=%20P%28-2.22%3C%20Z%3C2.22%29%20%3D%20P%28Z%3C2.22%29%20-P%28Z%3C-2.22%29%20%3D%200.9868%20-0.0132%3D%200.9736)
Step-by-step explanation:
Previous concepts
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".
The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".
Part a
Let X the random variable that represent the resitances of a population, and for this case we know the distribution for X is given by:
Where
and
We are interested on this probability
And the best way to solve this problem is using the normal standard distribution and the z score given by:
If we apply this formula to our probability we got this:
And we can find this probability with this difference and with the normal standard table or excel:
Part b
We select a sample size of n =4. And since the distribution for X is normal then we know that the distribution for the sample mean
is given by:
And we want this probability:
![P(0.11 < \bar X < 0.13)](https://tex.z-dn.net/?f=%20P%280.11%20%3C%20%5Cbar%20X%20%3C%200.13%29)
And we can use the z score defined by:
![z = \frac{\bar X -\mu}{\frac{\sigma}{\sqrt{n}}}](https://tex.z-dn.net/?f=%20z%20%3D%20%5Cfrac%7B%5Cbar%20X%20-%5Cmu%7D%7B%5Cfrac%7B%5Csigma%7D%7B%5Csqrt%7Bn%7D%7D%7D)
And using the limits we got:
![z = \frac{0.11-0.12}{\frac{0.009}{\sqrt{4}}}= -2.22](https://tex.z-dn.net/?f=%20z%20%3D%20%5Cfrac%7B0.11-0.12%7D%7B%5Cfrac%7B0.009%7D%7B%5Csqrt%7B4%7D%7D%7D%3D%20-2.22)
![z = \frac{0.13-0.12}{\frac{0.009}{\sqrt{4}}}= 2.22](https://tex.z-dn.net/?f=%20z%20%3D%20%5Cfrac%7B0.13-0.12%7D%7B%5Cfrac%7B0.009%7D%7B%5Csqrt%7B4%7D%7D%7D%3D%202.22)
And we want to find this probability:
![P(-2.22< Z](https://tex.z-dn.net/?f=%20P%28-2.22%3C%20Z%3C2.22%29%20%3D%20P%28Z%3C2.22%29%20-P%28Z%3C-2.22%29%20%3D%200.9868%20-0.0132%3D%200.9736)