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Fofino [41]
3 years ago
5

use the model to solve 4 friends equally split in one half of a pound of candy how much will each friend get​

Mathematics
2 answers:
jolli1 [7]3 years ago
4 0

Answer:

.25 pounds

Step-by-step explanation:

Unfortunately, the type of model that is supposed to be used is not given so I will just solve what I can. So each friend gets and equal amount. If we have 1 pound in the beginning, and 4 friends, then we have to divide the candy by 4. So we do 1 pound/4=.25 pounds. If you want to go above and beyond, we can always convert the pounds into ounces and make it so each friend gets 4 ounces of candy.

den301095 [7]3 years ago
3 0
Model-? I don't see a model?-
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Determine the distance between the following two points. (round to the nearest hundredths)
padilas [110]

Answer:

d=7.07

Step-by-step explanation:

Distance Formula: d=\sqrt{(x_2-x_1)^2+(y_2-y_1)^2}

Simply plug in the 2 coordinates into the distance formula to find distance <em>d</em>:

d=\sqrt{(3-(-2))^2+(-4-1)^2}

d=\sqrt{(3+2)^2+(-5)^2}

d=\sqrt{(5)^2+25}

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2 years ago
What are the primary differences between the different 1040 forms?
Rina8888 [55]
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8 0
3 years ago
Quadrilateral ABCD is a kite. If AD=AB, find AD<br><br> A. 5/3<br> B. 7<br> C. 5/7<br> D. 22
ICE Princess25 [194]
AD = AB

AD = 2r+8
AB = 5r-13

we need to find the value of r to find the total length

so 2r+8 = 5r-13
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r=7
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5 0
3 years ago
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lisov135 [29]

Answer:

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Step-by-step explanation:

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8 0
3 years ago
The Insurance Institute reports that the mean amount of life insurance per household in the US is $110,000. This follows a norma
nata0808 [166]

Answer:

a) \sigma_{\bar X} = \frac{\sigma}{\sqrt{n}}= \frac{40000}{\sqrt{50}}= 5656.85

b) Since the distribution for X is normal then we know that the distribution for the sample mean \bar X is given by:

\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})

c) P( \bar X >112000) = P(Z>\frac{112000-110000}{\frac{40000}{\sqrt{50}}}) = P(Z>0.354)

And we can use the complement rule and we got:

P(Z>0.354) = 1-P(Z

d) P( \bar X >100000) = P(Z>\frac{100000-110000}{\frac{40000}{\sqrt{50}}}) = P(Z>-1.768)

And we can use the complement rule and we got:

P(Z>-1.768) = 1-P(Z

e) P(100000< \bar X

And we can use the complement rule and we got:

P(-1.768

Step-by-step explanation:

a. If we select a random sample of 50 households, what is the standard error of the mean?

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".  

Let X the random variable that represent the amount of life insurance of a population, and for this case we know the distribution for X is given by:

X \sim N(110000,40000)  

Where \mu=110000 and \sigma=40000

If we select a sample size of n =35 the standard error is given by:

\sigma_{\bar X} = \frac{\sigma}{\sqrt{n}}= \frac{40000}{\sqrt{50}}= 5656.85

b. What is the expected shape of the distribution of the sample mean?

Since the distribution for X is normal then we know that the distribution for the sample mean \bar X is given by:

\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})

c. What is the likelihood of selecting a sample with a mean of at least $112,000?

For this case we want this probability:

P(X > 112000)

And we can use the z score given by:

z= \frac{\bar X  -\mu}{\frac{\sigma}{\sqrt{n}}}

And replacing we got:

P( \bar X >112000) = P(Z>\frac{112000-110000}{\frac{40000}{\sqrt{50}}}) = P(Z>0.354)

And we can use the complement rule and we got:

P(Z>0.354) = 1-P(Z

d. What is the likelihood of selecting a sample with a mean of more than $100,000?

For this case we want this probability:

P(X > 100000)

And we can use the z score given by:

z= \frac{\bar X  -\mu}{\frac{\sigma}{\sqrt{n}}}

And replacing we got:

P( \bar X >100000) = P(Z>\frac{100000-110000}{\frac{40000}{\sqrt{50}}}) = P(Z>-1.768)

And we can use the complement rule and we got:

P(Z>-1.768) = 1-P(Z

e. Find the likelihood of selecting a sample with a mean of more than $100,000 but less than $112,000

For this case we want this probability:

P(100000

And we can use the z score given by:

z= \frac{\bar X  -\mu}{\frac{\sigma}{\sqrt{n}}}

And replacing we got:

P(100000< \bar X

And we can use the complement rule and we got:

P(-1.768

8 0
3 years ago
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