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Dahasolnce [82]
3 years ago
12

The counting numbers, together with 0, are called the _ _ _ _ _ numbers.

Mathematics
1 answer:
german3 years ago
4 0

Answer:

Whole numbers

hope this helps!

Step-by-step explanation:

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Answer:

es 29.5?

Step-by-step explanation:

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3 years ago
Rachel walks to a friends house then to the store then back home. the distance from Rachel’s house to her friends house is 1 5/6
ryzh [129]

Answer:

11/12 miles hope this helps :)

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Samuel pays $20 for a shirt that is on sale. If he saved $5, what is the percent of the discount?
mamaluj [8]
Hi Kyliebreland!
So, If he saved $5 then we do $20 divided by $5 which gives 4 so now we split 100% into 4 pieces which gives us 25%.
Hope this helps!

7 0
3 years ago
Need help with this Question
vazorg [7]

Answer:

Step-by-step explanation:

W∩ X is asking for the intersection of sets W and X, or simply, which numbers can be found in both sets.

The numbers shared between the two sets are 3, 4, 5.

Next, we need to union this result with the set Y, or simply, combine the numbers in both sets together.

This would result in 2, 3, 4, 5

5 0
3 years ago
Thompson and Thompson is a steel bolts manufacturing company. Their current steel bolts have a mean diameter of 144 millimeters,
valentinak56 [21]

Answer:

The probability that the sample mean would differ from the population mean by more than 2.6 mm is 0.0043.

Step-by-step explanation:

According to the Central Limit Theorem if we have a population with mean μ and standard deviation σ and appropriately huge random samples (n > 30) are selected from the population with replacement, then the distribution of the sample  means will be approximately normally distributed.

Then, the mean of the distribution of sample mean is given by,

\mu_{\bar x}=\mu

And the standard deviation of the distribution of sample mean  is given by,

\sigma_{\bar x}=\frac{\sigma}{\sqrt{n}}

The information provided is:

<em>μ</em> = 144 mm

<em>σ</em> = 7 mm

<em>n</em> = 50.

Since <em>n</em> = 50 > 30, the Central limit theorem can be applied to approximate the sampling distribution of sample mean.

\bar X\sim N(\mu_{\bar x}=144, \sigma_{\bar x}^{2}=0.98)

Compute the probability that the sample mean would differ from the population mean by more than 2.6 mm as follows:

P(\bar X-\mu_{\bar x}>2.6)=P(\frac{\bar X-\mu_{\bar x}}{\sigma_{\bar x}} >\frac{2.6}{\sqrt{0.98}})

                           =P(Z>2.63)\\=1-P(Z

*Use a <em>z</em>-table for the probability.

Thus, the probability that the sample mean would differ from the population mean by more than 2.6 mm is 0.0043.

8 0
3 years ago
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