The shape of the distribution of the time required to get an oil change at a 20 minute oil change facility. However, records indicate the mean time is 21.3 minutes and the standard deviation is 3.9 minutes.

Suppose the manager agrees to pay each employee a $50 bonus if they meet a certain goal. On a typical Saturday, the oil-change facility will perform 40 oil changes between 10 A. M. and 12 P. M. Treating this as a random sample, at what mean oil-change time would there be a 10% chance of being at or below? This will be the goal established by the manager.

mean oil-change time would there be a 10% chance of being at or below.

Step-by-step explanation:

Given information:

Mean time

Standard deviation

standard error

10% chance of being at or below,

By use of standard normal table

,

By Central limit theorem,

On substitution,

Hence,mean oil-change time would there be a 10% chance of being at or below.

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For a mean oil change time of 20.51 minutes there would be a 10% chance of being at or below

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 normally distributed samples are 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.

In this problem, we have that:

Treating this as a random sample, at what mean oil-change time would there be a 10% chance of being at or below?

This is the 10th percentile, which is X when Z has a pvalue of 0.1. So it is X when Z = -1.28.

By the Central Limit Theorem

For a mean oil change time of 20.51 minutes there would be a 10% chance of being at or below

3 is greater then both of those numbers