Respuesta :
Answer:
The probability is 1 subtracted by the p-value of [tex]Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}[/tex], in which X is the value we want to find the probability of the sample mean exceeding, [tex]\mu[/tex] is the population mean, [tex]\sigma[/tex] is the standard deviation of the population and n is the size of the sample.
Step-by-step explanation:
To solve this question, we need to understand the normal probability distribution and the central limit theorem.
Normal Probability Distribution:
Problems of normal distributions can be solved using the z-score formula.
In a set with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the z-score of a measure X is given by:
[tex]Z = \frac{X - \mu}{\sigma}[/tex]
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.
Central Limit Theorem
The Central Limit Theorem establishes 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.
Central Limit Theorem for the sample mean:
Sample of size n, and thus:
[tex]s = \frac{\sigma}{\sqrt{n}}[/tex]
[tex]Z = \frac{X - \mu}{s} = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}[/tex]
Probability of the sample mean exceeding a value:
The probability is 1 subtracted by the p-value of [tex]Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}[/tex], in which X is the value we want to find the probability of the sample mean exceeding, [tex]\mu[/tex] is the population mean, [tex]\sigma[/tex] is the standard deviation of the population and n is the size of the sample.
