Answer:
a) ![P(20 \leq X \leq 30) = P(20-0.5 \leq X \leq 30+0.5)](https://tex.z-dn.net/?f=%20P%2820%20%5Cleq%20X%20%5Cleq%2030%29%20%3D%20P%2820-0.5%20%5Cleq%20X%20%5Cleq%2030%2B0.5%29)
![P(19.5 \leq X \leq 30.5) = P(\frac{19.5-25}{5} \leq Z \leq \frac{30.5 -25}{5})=P(-1.1 \leq Z \leq 1.1)](https://tex.z-dn.net/?f=%20P%2819.5%20%5Cleq%20X%20%5Cleq%2030.5%29%20%3D%20P%28%5Cfrac%7B19.5-25%7D%7B5%7D%20%5Cleq%20Z%20%5Cleq%20%5Cfrac%7B30.5%20-25%7D%7B5%7D%29%3DP%28-1.1%20%5Cleq%20Z%20%5Cleq%201.1%29)
And we can find this probability like this:
![P(-1.1 \leq Z \leq 1.1)= P(Z\leq 1.1) -P(Z\leq -1.1) = 0.864-0.136= 0.728](https://tex.z-dn.net/?f=P%28-1.1%20%5Cleq%20Z%20%5Cleq%201.1%29%3D%20P%28Z%5Cleq%201.1%29%20-P%28Z%5Cleq%20-1.1%29%20%3D%200.864-0.136%3D%200.728)
b) ![P(X \leq 30)= P(X](https://tex.z-dn.net/?f=P%28X%20%5Cleq%2030%29%3D%20P%28X%3C30.5%29)
And using the z score we got:
![P(X](https://tex.z-dn.net/?f=%20P%28X%3C30.5%29%20%3D%20P%28Z%3C%20%5Cfrac%7B30.5-25%7D%7B5%7D%29%20%3DP%28Z%3C1.1%29%20%3D%200.864)
c) ![P(X](https://tex.z-dn.net/?f=%20P%28X%3C30%29)
And if we use the continuity correction we got:
![P(X](https://tex.z-dn.net/?f=%20P%28X%3C30-0.5%29%20%3DP%28Z%3C%5Cfrac%7B29.5%20-25%7D%7B5%7D%29%3D%20P%28Z%3C0.9%29%20%3D0.816%20)
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".
Continuity correction means that we need to add and subtract 0.5 before standardizing the value specified.
Part a
Let X the random variable that represent the variable of interest of a population, and for this case we know the distribution for X is given by:
Where
and
Part a
For this case we want to find this probability:
![P(20 \leq X \leq 30) = P(20-0.5 \leq X \leq 30+0.5)](https://tex.z-dn.net/?f=%20P%2820%20%5Cleq%20X%20%5Cleq%2030%29%20%3D%20P%2820-0.5%20%5Cleq%20X%20%5Cleq%2030%2B0.5%29)
And if we use the z score given by:
![z =\frac{x-\mu}{\sigma}](https://tex.z-dn.net/?f=%20z%20%3D%5Cfrac%7Bx-%5Cmu%7D%7B%5Csigma%7D)
We got this:
![P(19.5 \leq X \leq 30.5) = P(\frac{19.5-25}{5} \leq Z \leq \frac{30.5 -25}{5})=P(-1.1 \leq Z \leq 1.1)](https://tex.z-dn.net/?f=%20P%2819.5%20%5Cleq%20X%20%5Cleq%2030.5%29%20%3D%20P%28%5Cfrac%7B19.5-25%7D%7B5%7D%20%5Cleq%20Z%20%5Cleq%20%5Cfrac%7B30.5%20-25%7D%7B5%7D%29%3DP%28-1.1%20%5Cleq%20Z%20%5Cleq%201.1%29)
And we can find this probability like this:
![P(-1.1 \leq Z \leq 1.1)= P(Z\leq 1.1) -P(Z\leq -1.1) = 0.864-0.136= 0.728](https://tex.z-dn.net/?f=P%28-1.1%20%5Cleq%20Z%20%5Cleq%201.1%29%3D%20P%28Z%5Cleq%201.1%29%20-P%28Z%5Cleq%20-1.1%29%20%3D%200.864-0.136%3D%200.728)
Part b
For this case we want this probability:
![P(X \leq 30)](https://tex.z-dn.net/?f=%20P%28X%20%5Cleq%2030%29)
And if we use the continuity correction we got:
![P(X \leq 30)= P(X](https://tex.z-dn.net/?f=P%28X%20%5Cleq%2030%29%3D%20P%28X%3C30.5%29)
And using the z score we got:
![P(X](https://tex.z-dn.net/?f=%20P%28X%3C30.5%29%20%3D%20P%28Z%3C%20%5Cfrac%7B30.5-25%7D%7B5%7D%29%20%3DP%28Z%3C1.1%29%20%3D%200.864)
Part c
For this case we want this probability:
![P(X](https://tex.z-dn.net/?f=%20P%28X%3C30%29)
And if we use the continuity correction we got:
![P(X](https://tex.z-dn.net/?f=%20P%28X%3C30-0.5%29%20%3DP%28Z%3C%5Cfrac%7B29.5%20-25%7D%7B5%7D%29%3D%20P%28Z%3C0.9%29%20%3D0.816%20)