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lina2011 [118]
3 years ago
13

The drawing of a building, shown below, has a scale of 1 in to 30 ft. what is the actual height, in ft, of the building

Mathematics
1 answer:
11111nata11111 [884]3 years ago
6 0
The answer is: 22.4 hope it helps :)
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jarptica [38.1K]

Answer:

A=15

B=13??

C=15

D=85

Step-by-step explanation:

B is the only one I'm not sure

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Which fraction is larger 2/3 or 3/4 and find the common denominator
tangare [24]

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3/4

Step-by-step explanation:

2/3=8/12

3/4=9/12

Common denominator: 12

larger fraction: 3/4

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HELP!!! What is the MAD for these numbers? 5 22 14 13 18 7 9 12
IgorLugansk [536]

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4.25

Step-by-step explanation:

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If a fair coin is tossed, the probability that it will land heads up is ½. Olivia tosses a coin 4 times, and it lands on heads 4
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Answer:

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Step-by-step explanation:

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6 0
3 years ago
At a new exhibit in the Museum of Science, people are asked to choose between 94 or 220 random draws from a machine. The machine
Tresset [83]

Answer:

0.0869 = 8.69% probability of getting more than 61% green balls.

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.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean \mu = p and standard deviation s = \sqrt{\frac{p(1-p)}{n}}

The machine is known to have 99 green balls and 78 red balls.

This means that p = \frac{99}{99+78} = 0.5593

Mean and standard deviation:

\mu = p = 0.5593

s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.5593*0.4407}{99+78}} = 0.0373

a. Calculate the probability of getting more than 61% green balls.

This is 1 subtracted by the pvalue of Z when X = 0.61. So

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

By the Central Limit Theorem

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

Z = \frac{0.61 - 0.5593}{0.0373}

Z = 1.36

Z = 1.36 has a pvalue of 0.9131

1 - 0.9131 = 0.0869

0.0869 = 8.69% probability of getting more than 61% green balls.

8 0
3 years ago
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