Answer:
We can be 95% certain that the sample mean will fall within $44.87 and $46.93
Step-by-step explanation:
Empirical Rule:
68% of the measures are within 1 standard deviation of the mean.
95% of the measures are within 2 standard deviation of the mean.
99.7% of the measures are within 3 standard deviations of the mean.
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.
Population:
Mean $45.90, standard deviation $10.31.
Sample:
By the Central limit theorem, mean $45.90, standard deviation [tex]s = \frac{10.31}{\sqrt{400}} = \frac{10.31}{20} = 0.5155[/tex]
Which interval can the branch manager be 95% certain that the sample mean will fall within?
By the Empirical Rule, within 2 standard deviations of the mean. So
45.90 - 2*0.5155 = $44.87
45.90 + 2*0.5155 = $46.93
We can be 95% certain that the sample mean will fall within $44.87 and $46.93
Answer:
Correct Answer (A)
We can be 95% certain that the sample mean will fall within $44.87 and $46.93
Step-by-step explanation:
Hope this helped :)
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