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Mumz [18]
2 years ago
8

Estimate the quotient for 627 ÷ 9. explain how you found your awnser

Mathematics
2 answers:
Nutka1998 [239]2 years ago
6 0

69.7

Step-by-step explanation:

627 ÷ 9

=69.66666666666666

approximately

69.7

Ronch [10]2 years ago
3 0

\sf \leadsto627 \div 9

\\  \\

\sf \leadsto \dfrac{627}{9}

\\  \\

\sf \leadsto \dfrac{627 \div 3}{9 \div 3}

\\  \\

\sf \leadsto \dfrac{209}{3}

\\  \\

\sf \leadsto 69.67

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Describe the location of the point having the following coordinates. negative abscissa, zero ordinate between Quadrant II and Qu
Marianna [84]

Answer:

The location of the point is between Quadrant II and Quadrant III

Step-by-step explanation:

we know that

The abscissa refers to the x-axis  and ordinate refers to the y-axis

so

in this problem we have

the coordinates of the point are (-x,0)

see the attached figure to better understand the problem

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6 0
3 years ago
A ski lift is designed with a total load limit of 20,000 pounds. It claims a capacity of 100 persons. An expert in ski lifts thi
Yanka [14]

Answer:

0.5 = 50% probability that a random sample of 100 independent persons will cause an overload

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 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 the sum of n values of a distribution, the mean is \mu \times n and the standard deviation is \sigma\sqrt{n}

An expert in ski lifts thinks that the weights of individuals using the lift have expected weight of 200 pounds and standard deviation of 30 pounds. 100 individuals.

This means that \mu = 200*100 = 20000, \sigma = 30\sqrt{100} = 300

If the expert is right, what is the probability that a random sample of 100 independent persons will cause an overload

Total load of more than 20,000 pounds, which is 1 subtracted by the pvalue of Z when X = 20000. So

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

Z = \frac{20000 - 20000}{300}

Z = 0

Z = 0 has a pvalue of 0.5

1 - 0.5 = 0.5

0.5 = 50% probability that a random sample of 100 independent persons will cause an overload

5 0
3 years ago
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