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Korvikt [17]
3 years ago
10

Plz help me with this

Mathematics
2 answers:
GuDViN [60]3 years ago
6 0

Answer:

h = \frac{V}{r^2\pi }

Step-by-step explanation:

Given

V = πr²h

Isolate h by dividing both sides by πr²

h = \frac{V}{r^2\pi } → C

stich3 [128]3 years ago
3 0

Answer:   \bold{c)\quad h=\dfrac{V}{\pi r^2} }

<u>Step-by-step explanation:</u>

V=\pi r^2h\\\\\text{In order to isolate h, divide both sides by }\pi r^2\\\large\boxed{\dfrac{V}{\pi r^2}=h}

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What’s 55 x 3squared to the root of 5
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So.. 55x3=165
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Explain how to round a five-digit number to the nearest ten thousand
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The Aluminum Association reports that the average American uses 56.8 pounds of aluminum in a year. A random sample of 49 househo
KonstantinChe [14]

Answer:

a) 10.38% probability that the sample mean will be more than 59 pounds.

b) 67.72% probability that the sample mean will be more than 56 pounds.

c) 22.10% probability that the sample mean will be between 56 and 57 pounds.

d) 1.46% probability that the sample mean will be less than 53 pounds.

e) 0% probability that the sample mean will be less than 49 pounds.

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.

In this question, we have that:

\mu = 56.8, \sigma = 12.2, n = 49, s = \frac{12.2}{\sqrt{49}} = 1.74285

a. More than 59 pounds

This is 1 subtracted by the pvalue of Z when X = 59. So

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

By the Central Limit Theorem

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

Z = \frac{59 - 56.8}{1.74285}

Z = 1.26

Z = 1.26 has a pvalue of 0.8962.

1 - 0.8962 = 0.1038

10.38% probability that the sample mean will be more than 59 pounds.

b. More than 56 pounds

This is 1 subtracted by the pvalue of Z when X = 56. So

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

Z = \frac{56 - 56.8}{1.74285}

Z = -0.46

Z = -0.46 has a pvalue of 0.3228.

1 - 0.3228 = 0.6772

67.72% probability that the sample mean will be more than 56 pounds.

c. Between 56 and 57 pounds

This is the pvalue of Z when X = 57 subtracted by the pvalue of Z when X = 56. So

X = 57

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

Z = \frac{57 - 56.8}{1.74285}

Z = 0.11

Z = 0.11 has a pvalue of 0.5438

X = 56

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

Z = \frac{56 - 56.8}{1.74285}

Z = -0.46

Z = -0.46 has a pvalue of 0.3228.

0.5438 - 0.3228 = 0.2210

22.10% probability that the sample mean will be between 56 and 57 pounds.

d. Less than 53 pounds

This is the pvalue of Z when X = 53.

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

Z = \frac{53 - 56.8}{1.74285}

Z = -2.18

Z = -2.18 has a pvalue of 0.0146

1.46% probability that the sample mean will be less than 53 pounds.

e. Less than 49 pounds

This is the pvalue of Z when X = 49.

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

Z = \frac{49 - 56.8}{1.74285}

Z = -4.48

Z = -4.48 has a pvalue of 0.

0% probability that the sample mean will be less than 49 pounds.

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Answer:all

Step-by-step explanation:

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