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DENIUS [597]
3 years ago
13

How many triangles exist with the given side lengths? 3 cm, 5 cm, 9 cm A. Exactly one unique triangle exists with the given side

lengths. B. More than one unique triangle exists with the given side lengths. C. No triangle exists with the given side lengths.
Mathematics
2 answers:
sattari [20]3 years ago
6 0

Answer:c is the correct ans

Step-by-step explanation:

Feel pleasure to help u

Fudgin [204]3 years ago
4 0

Answer:

B-. More than one unique triangle exists with the given side lengths.

Step-by-step explanation:

according to to tringle inequality theorem he sum of the length of two sides of a triangle should be greater than the third side”. In order to verify the mentioned theorem, some cal. are preforemed below

12+15>18

15+18>12

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alexdok [17]

Answer:

1 yes

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4no

Step-by-step explanation:

for every x value you should have a unique y value

5 0
3 years ago
Bottles filled by a certain machine are supposed to contain 12 oz of liquid. In fact the fill volume is random with mean 12.01 o
stepan [7]

Answer:

27.43% probability that the mean volume of a random sample of 144 bottles is less than 12 oz.

Step-by-step explanation:

To solve this problem, it is important 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 random variable X, with mean \mu and standard deviation \sigma, a large sample size can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}

In this problem, we have that:

\mu = 12.01, \sigma = 0.2, n = 144, s = \frac{0.2}{\sqrt{144}} = 0.0167

What is the probability that the mean volume of a random sample of 144 bottles is less than 12 oz

This is the pvalue of Z when X = 12

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

By the Central Limit Theorem

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

Z = \frac{12 - 12.01}{0.0167}

Z = -0.6

Z = -0.6 has a pvalue of 0.2743.

So there is a 27.43% probability that the mean volume of a random sample of 144 bottles is less than 12 oz.

4 0
3 years ago
Multiplying mixed numbers: 2 3/4 • 8 1/4
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2 3/4 x 8 1/4 = <u>22 11/16</u>

hope this helps!

7 0
4 years ago
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I'm estimating that this is about the 5th or 6th time that I've seen this
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5 0
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Kate is saving money to take an SAT prep course. So far, she has $180 saved, and she plans to add $17 each month.
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It will take her 10 weeks to save enough money. 350-180=170 170/17=10 Hope this helps!


8 0
4 years ago
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