Answer:
0.0475 = 4.75% probability of randomly picking a sample of n =25, 1-pound bags that average more than M = 13 shrimp per bag.
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
and standard deviation
, the zscore of a measure X is given by:
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 pvalue, we get the probability that the value of the measure is greater than X.
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean
and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
Single bag:
Sample of 25 bags:
What is the probability of randomly picking a sample of n =25, 1-pound bags that average more than M = 13 shrimp per bag?
This is 1 subtracted by the pvalue of Z when X = 13. So
By the Central Limit Theorem
has a pvalue of 0.9525
1 - 0.9525 = 0.0475
0.0475 = 4.75% probability of randomly picking a sample of n =25, 1-pound bags that average more than M = 13 shrimp per bag.