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nika2105 [10]
3 years ago
15

How do you multiply 1/16 x 16

Mathematics
2 answers:
murzikaleks [220]3 years ago
8 0
16/16 = 1 is the answer to your given question.
ehidna [41]3 years ago
5 0

You can rewrite the 16 as a fraction: 16/1  

This works since 16/1 is still equal to 16.

(You can rewrite any whole number as a fraction over 1:  number = \frac{number}{1} )

Now just multiply the two fractions as normal:

\frac{1}{16} \times \frac{16}{1}      <em>(Numerator times numerator, and denominator times denominator)</em>

= \frac{16}{16}

= 1

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The amount people pay for cable service varies quite a bit but the mean monthly fee is $142 and the standard deviation is $29. t
zhuklara [117]

Answer:

a) By the Central Limit Theorem, the mean is $142 and the standard deviation is $0.7488.

b) By the Central Limit Theorem, approximately normal.

c) 0.0901 = 9.01% probability that the average cable service paid by the sample of cable service customers will exceed $143

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

When the distribution is normal, we use 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.

The mean monthly fee is $142 and the standard deviation is $29.

This means that \mu = 142, \sigma = 29

Part a: what are the mean an standard deviation of the sample distribution of x hat show your work and justify your reasoning.

Sample of 1500(larger than 30).

By the Central Limit Theorem

The mean is $142

The standard deviation is s = \frac{29}{\sqrt{1500}} = 0.7488

Part b: what is the shape of the sampling distribution of x hat justify your answer.

By the Central Limit Theorem, approximately normal.

Part C: what is the probability that the average cable service paid by the sample of cable service customers will exceed $143?

This is 1 subtracted by the pvalue of Z when X = 143. So

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

By the Central Limit Theorem

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

Z = \frac{143 - 142}{0.7488}

Z = 1.34

Z = 1.34 has a pvalue of 0.9099

1 - 0.9099 = 0.0901

0.0901 = 9.01% probability that the average cable service paid by the sample of cable service customers will exceed $143

4 0
3 years ago
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