Respuesta :
Answer:
a) For this case we have the following info:
[tex] \mu = 130 , sigma= 8[/tex]
And we select a sample size of 6. Assuming that the data can be approximated to a normal distribution the sample mean have the following parameters:
[tex] \mu_{\bar X} = 130[/tex]
[tex]\sigma_{\bar X} = \frac{8}{\sqrt{6}}=3.266[/tex]
b) For this case assuming a nomal distribution then the sample mean have the following distribution:
[tex] \bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}}) [/tex]
c) [tex] P(\bar X>140)[/tex]
And we can use the z score formula given by:
[tex] z = \frac{140-130}{\frac{8}{\sqrt{6}}}= 3.062[/tex]
And using the complement rule we have:
[tex] P(Z>3.062) =1-P(Z<3.062) = 1-0.9989= 0.0011[/tex]
Step-by-step explanation:
Part a
For this case we have the following info:
[tex] \mu = 130 , sigma= 8[/tex]
And we select a sample size of 6. Assuming that the data can be approximated to a normal distribution the sample mean have the following parameters:
[tex] \mu_{\bar X} = 130[/tex]
[tex]\sigma_{\bar X} = \frac{8}{\sqrt{6}}=3.266[/tex]
Part b
For this case assuming a nomal distribution then the sample mean have the following distribution:
[tex] \bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}}) [/tex]
Part c
We want this probability:
[tex] P(\bar X>140)[/tex]
And we can use the z score formula given by:
[tex] z = \frac{140-130}{\frac{8}{\sqrt{6}}}= 3.062[/tex]
And using the complement rule we have:
[tex] P(Z>3.062) =1-P(Z<3.062) = 1-0.9989= 0.0011[/tex]