Respuesta :
Answer:
a) [tex]\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})[/tex]
[tex]\bar X \sim N(\mu=123,\sigma_{\bar X} = \frac{0.08}{\sqrt{3}}= 0.046)[/tex]
b) [tex]P(\bar X >124)=P(Z>\frac{124-123}{\frac{0.08}{\sqrt{3}}}=21.65)[/tex]
And using a calculator, excel or the normal standard table we have that:
[tex]P(Z>21.65) \approx 0[/tex]
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".
Solution to the problem
Part a
Let X the random variable that represent the weights of a population, and for this case we know the distribution for X is given by:
[tex]X \sim N(123,0.08)[/tex]
Where [tex]\mu=123[/tex] and [tex]\sigma=0.08[/tex]
Since the distribution for X is normal then we know that the distribution for the sample mean [tex]\bar X[/tex] is given by:
[tex]\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})[/tex]
[tex]\bar X \sim N(\mu=123,\sigma_{\bar X} = \frac{0.08}{\sqrt{3}}= 0.046)[/tex]
Part b
We want to find this probability:
[tex] P(\bar X >124)[/tex]
And we can use the z score given by:
[tex] z = \frac{\bar X -\mu}{\sigma_{\bar X}}[/tex]
And using this formula we got:
[tex]P(\bar X >124)=P(Z>\frac{124-123}{\frac{0.08}{\sqrt{3}}}=21.65)[/tex]
And using a calculator, excel or the normal standard table we have that:
[tex]P(Z>21.65) \approx 0[/tex]