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True [87]
3 years ago
9

A neighbor farmer had some chickens. She had 24 feet of wire fencing to

Mathematics
1 answer:
Paraphin [41]3 years ago
8 0

Answer:

A rectangle is defined by its length = L, and its width = W.

So the perimeter of the of the rectangle can be written as:

Perimeter = 2*L + 2*W.

In this case, we want to leave the perimeter fixed, so we have:

24ft = 2*L + 2*W.

Now, we do not have any other restrictions, so to know the different dimensions now we can write this as a function, by isolating one of the variables.

2*L = 24ft - 2*W

L = 12ft - W.

or:

L(W) = 12ft - W.

Such that:

W must be greater than zero (because we can not have negative or zero width).

And W must be smaller than 12ft (because in that case we would have zero or negative length)

Then the possible different dimensions are given by:

L(W) = 12ft - W

0ft < W < 12ft.

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The operation manager at a tire manufacturing company believes that the mean mileage of a tire is 33,208 miles, with a standard
avanturin [10]

Answer:

There is a 92.32% probability that the sample mean would differ from the population mean by less than 633 miles in a sample of 49 tires if the manager is correct.

Step-by-step explanation:

Problems of normally distributed samples can be 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.

Central Limit Theorem

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

In this problem, we have that:

The operation manager at a tire manufacturing company believes that the mean mileage of a tire is 33,208 miles, with a standard deviation of 2503 miles.

This means that \mu = 33208, \sigma = 2503.

What is the probability that the sample mean would differ from the population mean by less than 633 miles in a sample of 49 tires if the manager is correct?

This is the pvalue of Z when X = 33208+633 = 33841 subtracted by the pvalue of Z when X = 33208 - 633 = 32575

By the Central Limit Theorem, we have t find the standard deviation of the sample, that is:

s = \frac{\sigma}{\sqrt{n}} = \frac{2503}{\sqrt{49}} = 357.57

So

X = 33841

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

Z = \frac{33841 - 33208}{357.57}

Z = 1.77

Z = 1.77 has a pvalue of 0.9616

X = 32575

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

Z = \frac{32575- 33208}{357.57}

Z = -1.77

Z = -1.77 has a pvalue of 0.0384.

This means that there is a 0.9616 - 0.0384 = 0.9232 = 92.32% probability that the sample mean would differ from the population mean by less than 633 miles in a sample of 49 tires if the manager is correct.

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