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nika2105 [10]
2 years ago
9

D

Mathematics
1 answer:
ss7ja [257]2 years ago
5 0

The area of the <em>irregular</em> quadrilateral ABCD is equal to 234 square centimeters. (Correct choice: C)

<h3>What is the area of the quadrilateral?</h3>

Herein we have a description of an <em>irregular</em> quadrilateral, whose area must be determined by adding the areas of minor quadrilaterals and triangles that are part of it. The area is now determined:

A = 0.5 · (24 cm) · (7 cm) + 0.5 · (15 cm) · (20 cm)

A = 234 cm²

The area of the <em>irregular</em> quadrilateral ABCD is equal to 234 square centimeters. (Correct choice: C)

<h3>Remark</h3>

The picture with the quadrilateral is missing and is included as attachment.

To learn more on quadrilaterals: brainly.com/question/13805601

#SPJ1

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four angles at point B in this second diagram.

Step-by-step explanation:

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A manager at a local manufacturing company has been monitoring the output of one of the machines used to manufacture chromium sh
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0.682 = 68.2% probability that the average length of these 16 shells will be between 116 and 120 centimeters when the machine is operating "properly".

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 normal distributions can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the z-score 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 p-value, 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 \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.

Normally distributed with a mean of 118 centimeters and a standard deviation of 8 centimeters.

This means that \mu = 118, \sigma = 8

Sample of 16 shells

This means that n = 16, s = \frac{8}{\sqrt{16}} = 2

What is the probability that the average length of these 16 shells will be between 116 and 120 centimeters when the machine is operating "properly?"

This is the pvalue of Z when X = 120 subtracted by the pvalue of Z when X = 116.

X = 120

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

By the Central Limit Theorem

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

Z = \frac{120 - 118}{2}

Z = 1

Z = 1 has a pvalue of 0.841

X = 116

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

Z = \frac{116 - 118}{2}

Z = -1

Z = -1 has a pvalue of 0.159

0.841 - 0.159 = 0.682

0.682 = 68.2% probability that the average length of these 16 shells will be between 116 and 120 centimeters when the machine is operating "properly".

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