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just olya [345]
3 years ago
7

How do I get the out number to 30?

Mathematics
1 answer:
Ne4ueva [31]3 years ago
5 0
Notice the pattern of the out bags. For every person after the first, three more bags are pulled. By continuing this pattern you will eventually find the answer

1=2
2=5
3=8
4=11
5=14
6=17
7=20
8=23
9=26
10=29
11=32

Therefore, you need 11 people to get the job done in one day
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Solve the equation. Check your solution.
Varvara68 [4.7K]

Answer:

17/7

Step-by-step explanation:

2g+2(-8+2g)=1-g

2g-16+4g=1-g

6g-16=1-g

6g-(-g)-16=1

6g+g=1+16

7g=17

g=17/7

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2 years ago
X over 3 equals 2 over 5
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Answer:

so

x = 6 over 5

Step-by-step explanation:

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The deepest part of the Mariana trench (the deepest trench in the world’s oceans) is at an elevation of -11.033 kilometers. The
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Answer:

Step-by-step explanation:

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3 years ago
In the library on a university campus, there is a sign in the elevator that indicates a limit of 16 persons. In addition, there
lesya692 [45]

Answer:

15.87% probability that a random sample of 16 people will exceed the weight limit

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.

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 sums, the theorem can be applied, with mean n*\mu and standard deviation s = \sqrt{n}*\sigma

In this problem, we have that:

n = 16, \mu = 16*150 = 2400, s = \sqrt{16}*27 = 108

If a random sample of 16 persons from the campus is to be taken, what is the chance that a random sample of 16 people will exceed the weight limit

This is 1 subtracted by the pvalue of Z when X = 2508. So

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

By the Central Limit Theorem

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

Z = \frac{2508 - 2400}{108}

Z = 1

Z = 1 has a pvalue of 0.8413

1 - 0.8413 = 0.1587

15.87% probability that a random sample of 16 people will exceed the weight limit

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3 years ago
Separate 53 people into two groups so that the first group has 7 fewer than 4 times the number of people in the second group.
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There would be a group of 12 and a group of 41
3 0
3 years ago
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