Respuesta :
Answer:
By the Central Limit Theorem, the sampling distribution is approximately normal, with mean 30 and standard deviation 0.1.
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]
The batteries produced in a manufacturing plant have a mean time to failure of 30 months with a standard deviation of 2 months.
This means that [tex]\mu = 30, \sigma = 2[/tex]
Sample of 400 batteries. The sampling distribution of is approximately:
So [tex]n = 400, s = \frac{\sigma}{\sqrt{n}} = \frac{2}{\sqrt{400}} = 0.1[/tex]
By the Central Limit Theorem, the sampling distribution is approximately normal, with mean 30 and standard deviation 0.1.
