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olya-2409 [2.1K]
3 years ago
15

An elevator has a stated maximum capacity of 12 people or 2004 pounds. If 12 people have weights with a mean greater than (2004/

12) = 167 pounds, the capacity will be exceeded. Assume that weights of men are normally distributed with a mean of 182.9 pounds and a standard deviation of 40.8 pounds. Show your work and round your answers to FOUR decimal places.a. Compute the probability that a randomly selected man will have a weight greater than 167 pounds.b. Compute the probability that 12 randomly selected men will have a mean weight that is greater than 167 pounds.c. Does the elevator appear to have the correct weight limit? Why or why not?
Mathematics
1 answer:
weqwewe [10]3 years ago
8 0

Answer:

a) 0.6517; b) 0.9115; c) No

Step-by-step explanation:

For part a, we will use the formula for a z score of an individual:

z=\frac{X-\mu}{\sigma}\\\\=\frac{167-182.9}{40.8}\\\\=\frac{-15.9}{40.8}\approx -0.39

Using a z table, we see that the area under the curve to the left of this value is 0.3483.  However, we want the probability greater than this, which is the area to the right of this value under the curve; this means we subtract from 1:

1-0.3483 = 0.6517

For part b, we will use the formula for a z score of the mean of a sample:

z=\frac{\bar{X}-\mu}{\sigma \div \sqrt{n}}\\\\=\frac{167-182.9}{40.8\div \sqrt{12}}\\\\=\frac{-15.9}{40.8\div 3.4641}\\\\=\frac{-15.9}{11.778}\approx -1.35

Using a z table, we see that the area under the curve to the left of this value is 0.0885.  This means the area under the curve to the right of this value is

1-0.0885 = 0.9115

For part c,

The fact that the probability that any 12 men on the elevator will have a mean weight greater than 167, putting their total weight above 2004 pounds, is 91% means the elevator does not have the appropriate limit.  There is a high chance the maximum will be exceeded.

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Answer:

a) The sample is larger than 30, so, by the Central Limit Theorem, the distribution of the sample means will be normally distributed with mean 38.4 and standard deviation 0.8538.

b) 0.25% probability that his average kicks is less than 36 yards

c) 0.11% probability that his average kicks is more than 41 yards

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d-b) 1.32% probability that his average kicks is less than 36 yards

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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 \mu and standard deviation \sigma, the zscore of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

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.

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The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

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:

\mu = 38.4, \sigma = 5.4, n = 40, s = \frac{5.4}{\sqrt{40}} = 0.8538

a. What is the distribution of the sample mean? Why?

The sample is larger than 30, so, by the Central Limit Theorem, the distribution of the sample means will be normally distributed with mean 38.4 and standard deviation 0.8538.

b. What is the probability that his average kicks is less than 36 yards?

This is the pvalue of Z when X = 36. So

Z = \frac{X - \mu}{\sigma}

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{36 - 38.4}{0.8538}

Z = -2.81

Z = -2.81 has a pvalue of 0.0025

0.25% probability that his average kicks is less than 36 yards

c. What is the probability that his average kicks is more than 41 yards?

This is 1 subtracted by the pvalue of Z when X = 41. So

Z = \frac{X - \mu}{s}

Z = \frac{41 - 38.4}{0.8538}

Z = 3.05

Z = 3.05 has a pvalue of 0.9989

1 - 0.9989 = 0.0011

0.11% probability that his average kicks is more than 41 yards

d. If the sample size is 25 in the above problem, what will be your answer to part (a) , (b)and (c)?

Now n = 25, s = \frac{5.4}{\sqrt{25}} = 1.08

So

a)

The sample is larger than 30, so, by the Central Limit Theorem, the distribution of the sample means will be normally distributed with mean 38.4 and standard deviation 1.08

b)

Z = \frac{X - \mu}{s}

Z = \frac{36 - 38.4}{1.08}

Z = -2.22

Z = -2.22 has a pvalue of 0.0132

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c)

Z = \frac{X - \mu}{s}

Z = \frac{41 - 38.4}{1.08}

Z = 2.41

Z = 2.41 has a pvalue of 0.9920

1 - 0.9920 = 0.0080

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