Respuesta :
Answer:
1) [tex]P(X<260)=P(\frac{X-\mu}{\sigma}<\frac{260-\mu}{\sigma})=P(Z<\frac{260-266}{16})=P(Z<-0.375)[/tex]
And we can find this probability using the normal standar ddistirbution or excel:
[tex]P(Z<-0.375)=P(Z<-0.375)=0.354[/tex]
2) [tex] \mu_{\bar X} = \mu=266[/tex]
3) [tex]\sigma_{\bar x}= \frac{\sigma}{\sqrt{n}}= \frac{16}{\sqrt{60}}=2.07[/tex]
4) [tex]\bar X \sim N(\mu=266, \sigma_{\bar X}= \frac{\sigma}{\sqrt{n}}=2.07)[/tex]
5) [tex]P(\bar X <260)=P(Z<\frac{260-266}{\frac{16}{\sqrt{60}}}=-2.905)[/tex]
And using a calculator, excel ir the normal standard table we have that:
[tex]P(Z<-2.905)=0.00183[/tex]
This probability is different because for this case we are finding the probability that the sample mean with 60 observations would be less than 260, and from part 1 we were finding an individual probability.
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 1
Let X the random variable that represent the duration of the pregnancy of a population, and for this case we know the distribution for X is given by:
[tex]X \sim N(266,16)[/tex]
Where [tex]\mu=266[/tex] and [tex]\sigma=16[/tex]
We are interested on this probability
[tex]P(X<260)[/tex]
And the best way to solve this problem is using the normal standard distribution and the z score given by:
[tex]z=\frac{x-\mu}{\sigma}[/tex]
[tex]P(X<260)=P(\frac{X-\mu}{\sigma}<\frac{260-\mu}{\sigma})=P(Z<\frac{260-266}{16})=P(Z<-0.375)[/tex]
And we can find this probability using the normal standar distribution or excel:
[tex]P(Z<-0.375)=P(Z<-0.375)=0.354[/tex]
Part 2
For this case the mean of the sampling distribution is:
[tex] \mu_{\bar X} = \mu=266[/tex]
Part 3
The standard deviation for the sample mean is given by:
[tex]\sigma_{\bar x}= \frac{\sigma}{\sqrt{n}}= \frac{16}{\sqrt{60}}=2.07[/tex]
Part 4
The distribution for the sample mean [tex]\bar X[/tex] is given by:
[tex]\bar X \sim N(\mu=266, \sigma_{\bar X}= \frac{\sigma}{\sqrt{n}}=2.07)[/tex]
Part 5
We can find the probabilitiy like this:
[tex]P(\bar X <260)=P(Z<\frac{260-266}{\frac{16}{\sqrt{60}}}=-2.905)[/tex]
And using a calculator, excel ir the normal standard table we have that:
[tex]P(Z<-2.905)=0.00183[/tex]
This probability is different because for this case we are finding the probability that the sample mean with 60 observations would be less than 260, and from part 1 we were finding an individual probability.