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.

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Answer:

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 [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the zscore 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 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 [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.

In this problem, we have that:

[tex]\mu = 21.3, \sigma = 3.9, n = 40, s = \frac{3.9}{\sqrt{40}} = 0.6166[/tex]

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.

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

By the Central Limit Theorem

[tex]Z = \frac{X - \mu}{s}[/tex]

[tex]-1.28 = \frac{X - 21.3}{0.6166}[/tex]

[tex]X - 21.3 = -1.28*0.6166[/tex]

[tex]X = 20.51[/tex]

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

Answer:

[tex]20.51 \rm min[/tex] mean​ oil-change time would there be a​ 10% chance of being at or​ below.

Step-by-step explanation:

Given information:

Mean time [tex]\mu=21.3\rm min[/tex]

Standard deviation [tex]\sigma=3.9\rm min[/tex]

[tex]n=40[/tex]

standard error [tex]s=\frac{\sigma}{\sqrt n}=\frac{3.9}{40}=0.6166[/tex]

10% chance of being at or​ below,[tex]\widehat{p}=0.1[/tex]

By use of standard normal table

[tex]\widehat{p}=0.1[/tex],[tex]z=-1.282[/tex]

By Central limit theorem,

[tex]z=\frac{X-\mu}{s}[/tex]

On substitution,

[tex]-1.282=\frac{X-21.3}{\ 0.6166}[/tex]

[tex]X=20.51 \rm min[/tex]

Hence,[tex]20.51 \rm min[/tex] mean​ oil-change time would there be a​ 10% chance of being at or​ below.

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