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maks197457 [2]
2 years ago
8

Evaluate ∛27 9 81 3 24

Mathematics
2 answers:
liq [111]2 years ago
5 0

Answer:

C. 3

Step-by-step explanation:

The cube root of 27 is 3 because

3 x 3 x 3 =27

9 x 3 = 27

so your answer is 3

hope this helped!

Anna71 [15]2 years ago
4 0
3 because 3•3 is 9 and 9•3 is 27
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If 1/6x + 3 = 7 what is x
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Answer:

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Step-by-step explanation:

1/6 x + 3 = 7

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Every employee works 7 hours per day. every employee works 5 days per week. every employee works 49 weeks per year. how many day
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To determine the number of days that an employee work in a week, we simply use dimensional analysis and multiplying the number of works per week with the number of weeks in total for a year. That is,
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How do I do this help please
elena55 [62]

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4 0
3 years ago
Suppose that 20% of the residents in a certain state support an increase in the property tax. An opinion poll will randomly samp
aleksklad [387]

Answer:

95.44% probability the resulting sample proportion is within .04 of the true proportion.

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 the sampling distribution of the sample proportion in sample of size n, the mean is \mu = p and the standard deviation is s = \sqrt{\frac{p(1-p)}{n}}

In this question:

p = 0.2, n = 400

So

\mu = 0.2, s = \sqrt{\frac{0.2*0.8}{400}} = 0.02

How likely is the resulting sample proportion to be within .04 of the true proportion (i.e., between .16 and .24)?

This is the pvalue of Z when X = 0.24 subtracted by the pvalue of Z when X = 0.16.

X = 0.24

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

By the Central Limit Theorem

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

Z = \frac{0.24 - 0.2}{0.02}

Z = 2

Z = 2 has a pvalue of 0.9772.

X = 0.16

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

Z = \frac{0.16 - 0.2}{0.02}

Z = -2

Z = -2 has a pvalue of 0.0228.

0.9772 - 0.0228 = 0.9544

95.44% probability the resulting sample proportion is within .04 of the true proportion.

6 0
3 years ago
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