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kolbaska11 [484]
3 years ago
8

Simplify. Remove all perfect squares from inside the square root. √180

Mathematics
2 answers:
miskamm [114]3 years ago
3 0

Answer:

13.42

Step-by-step explanation:

\sqrt{180} =6\sqrt{5} \\\\6\sqrt{5} =13.42

ch4aika [34]3 years ago
3 0

Answer:

answer: 6\sqrt{5}

Step-by-step explanation:

\sqrt{180}  = \sqrt{4 * 9 * 5} \\= 2*3\sqrt{5} \\= 6\sqrt{5}

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140/2=70

70x1=70

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2 years ago
I need help with this math question 7+(3•5)
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Answer:

The answer is 22

Step-by-step explanation:

(3 • 5) = 15

7 + 15 = 22

22 is your answer

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What does this image show? A) How to add. B) Information about exponential notation. C) Bow to multiply and divide. Will Mark Br
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B) Information about exponential notation

Step-by-step explanation:

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2 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.

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2 years ago
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