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nadya68 [22]
3 years ago
11

Solve for X and determine the measure of each angle. X (x - 35) X (2x - 75°)

Mathematics
2 answers:
Usimov [2.4K]3 years ago
3 0

it's a quadrilateral

interior angles add up to 360

x + 2x - 75 + x + x - 35 = 360

5x - 110 = 360

5x = 360 + 110

x = 470 ÷ 5

x = 95

and x - 35 = 60

2x - 75

= 190 -75

= 115

Aliun [14]3 years ago
3 0

Answer:

see explanation

Step-by-step explanation:

The sum of the interior angles of a quadrilateral = 360°

Sum the given angles and equate to 360

x + x + x - 35 + 2x - 75 = 360, that is

5x - 110 = 360 ( add 110 to both sides )

5x = 470 ( divide both sides by 5 )

x = 94 , then

x - 35 = 94 - 35 = 59

2x - 75 = 2(94) - 75 = 188 - 75 = 113

Thus

The 4 angles are 59°, 94°, 94°, 113°

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6(x-2)=-18 what does x=
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Answer:

x = -1

Step by step explanation:

Distribute 6

6x-12= -18

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Divide both sides by 6

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Hope this helps

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3 years ago
Solve the problem. Use the Central Limit Theorem.The annual precipitation amounts in a certain mountain range are normally distr
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Answer:

0.8944 = 89.44% probability that the mean annual precipitation during 25 randomly picked years will be less than 112 inches.

Step-by-step explanation:

To solve this question, we use 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 establishes 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.

Mean of 109.0 inches, and a standard deviation of 12 inches.

This means that \mu = 109, \sigma = 12

Sample of 25.

This means that n = 25, s = \frac{12}{\sqrt{25}} = 2.4

What is the probability that the mean annual precipitation during 25 randomly picked years will be less than 112 inches?

This is the p-value of Z when X = 112. So

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

By the Central Limit Theorem

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

Z = \frac{112 - 109}{2.4}

Z = 1.25

Z = 1.25 has a p-value of 0.8944.

0.8944 = 89.44% probability that the mean annual precipitation during 25 randomly picked years will be less than 112 inches.

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