Answer:
0.6121 = 61.21% probability that the sample average amount of time taken on each day is at most 11 min.
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 z-score 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 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
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.
Normal distribution with mean value 10 min and standard deviation 3 min.
This means that
First day:
5 individuals, so
The probability is the p-value of Z when X = 11. So
By the Central Limit Theorem
has a p-value of 0.7719.
Second day:
6 individuals, so
has a p-value of 0.793.
What is the probability that the sample average amount of time taken on each day is at most 11 min?
Each day is independent of other days, so we multiply the probabilities.
0.7719*0.793 = 0.6121
0.6121 = 61.21% probability that the sample average amount of time taken on each day is at most 11 min.