Suzanna wants to fill a cylindrical vase 2/3 of the way with water before placing her flowers in it. What is the approximate vol

ume of water in the vase if the vase has a height of 14 cm and a radius of 5 centimeters? Use 3.14 for π and round your answer to the nearest tenth. The approximate volume of water in the vase is __________ cm^3.

Mathematics

1 answer:

FrozenT [24]3 years ago

6 0

$\bf \textit{volume of a cylinder}\\\\ V=\pi r^2 h~~ \begin{cases} r=radius\\ h=height\\[-0.5em] \hrulefill\\ r=5\\ h=14 \end{cases}\implies V=\pi (5)^2(14)\implies V=350\pi \\\\\\ \stackrel{\textit{and }\frac{2}{3}\textit{ of that whole volume is}}{\cfrac{2}{3}(350\pi )}\implies \cfrac{700\pi }{3}\implies \stackrel{\textit{using }\pi =3.14}{\cfrac{2198}{3}}\implies \stackrel{\textit{rounded up}}{732.7}$

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Pls answer quick;-; 20 points!

dexar [7]

Answer:65

Step-by-step explanation:

It’s 65 bc 35+80= 115. And triangles are 180° so 115-180= 65.

Hope it helped

5 0

2 years ago

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Suppose babies born in a large hospital have a mean weight of 3215 grams, and a variance of 84,681. If 67 babies are sampled at

Drupady [299]

Answer:

0.8558 = 85.58% probability that the mean weight of the sample babies would differ from the population mean by less than 52 grams.

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 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 $s = \frac{\sigma}{\sqrt{n}}$ .