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Andrew [12]
2 years ago
12

32 = 4(n – 5) and -s + 11 = 17 please solved Both of the following​

Mathematics
2 answers:
il63 [147K]2 years ago
7 0

Answer:

Step-by-step explanation:

Let's solve your system by substitution.

32=4(n−5);−s+11=17

Rewrite equations:

32=4n−20;−s+11=17

Step: Solve 32=4n−20 for n:

32=4n−20

32+−4n=4n−20+−4n(Add -4n to both sides)

−4n+32=−20

−4n+32+−32=−20+−32(Add -32 to both sides)

−4n=−52

−4n/ −4 =/ −52/ −4

(Divide both sides by -4)

n=13

Step: Substitute 13 for n in  −s+11=17:

−s+11=17

−s+11=17

−s+11+−11=17+−11(Add -11 to both sides)

−s=6

−s/ −1 = 6/ −1

(Divide both sides by -1)

s=−6

Answer:

n=13 and s=−6

 

valina [46]2 years ago
4 0

Answer:

n=13 s=-6

Step-by-step explanation:

Hope this helps :)

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Answer:

0.9452 = 94.52% probability that their mean length is less than 16.8 inches.

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:

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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 establishes 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.

Mean of 15.4 inches, and standard deviation of 3.5 inches.

This means that \mu = 15.4, \sigma = 3.5

16 items are chosen at random

This means that n = 16, s = \frac{3.5}{\sqrt{16}} = 0.875

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This is the p-value of Z when X = 16.8. So

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

By the Central Limit Theorem

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

Z = \frac{16.8 - 15.4}{0.875}

Z = 1.6

Z = 1.6 has a p-value of 0.9452.

0.9452 = 94.52% probability that their mean length is less than 16.8 inches.

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Step-by-step explanation:

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