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Sagot :
Answer:
The mean of the sampling distribution of p is 0.25 and the standard deviation is 0.0685.
Step-by-step explanation:
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]
25% of the approximately 1000 issues reported per month that require more than one call.
This means that [tex]p = 0.25[/tex]
What are the mean and standard deviation of the sampling distribution of p?
Sample of 40 means that [tex]n = 40[/tex].
By the Central Limit Theorem,
The mean is [tex]\mu = p = 0.25[/tex]
The standard deviation is [tex]s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.25*0.75}{40}} = 0.0685[/tex]
The mean of the sampling distribution of p is 0.25 and the standard deviation is 0.0685.
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