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Kaylis [27]
3 years ago
14

Find the inverse of the given relation. {(9, –10), (2, –2), (8, –7), (13, –13)}

Mathematics
1 answer:
aleksklad [387]3 years ago
8 0

Answer:

The inverse of the relation is {(-10 , 9) , (-2 , 2) , (-7 , 8) , (-13 , 13)} ⇒ 3rd

Step-by-step explanation:

- The inverse relation is a relation that we switched their inputs and

  outputs

- A relation R1 is the inverse of a relation R if y = R (x) then x = R1 (y).

- In R = f(x), x is the input and y is the output , in x = R1 (y), y is the input

 and x is the output

- Ex: The inverse of the relation R(x) = {(-2 , 3) , (1 , 5) , (3 , 0)} is

 R1(x) = {(3 , -2) , (5 , 1) , (0 , 3)}

* Lets solve the problem

- The relation is {(9 , -10) , (2 , -2) , (8 , -7) , (13 , -13)}

∵ x = 9 , 2 , 8 , 13

∵ y = -10 , -2 , -7 , -13

- To make the inverse relation we will switch x and y

∴ In the inverse relation

  x = -10 , -2 , -7 , -13

  y = 9 , 2 , 8 , 13

∴ The inverse of the relation is {(-10 , 9) , (-2 , 2) , (-7 , 8) , (-13 , 13)}

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Answer:

5.7

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Convert the following <br><br>250cm to m​
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In 2008 the Better Business Bureau settled 75% of complaints they received (USA Today, March 2, 2009). Suppose you have been hir
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Explained below.

Step-by-step explanation:

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The mean of this sampling distribution of sample proportion is:

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The standard deviation of this sampling distribution of sample proportion is:

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(a)

The sample selected is of size <em>n</em> = 450 > 30.

Then according to the central limit theorem the sampling distribution of sample proportion is normally distributed.

The mean and standard deviation are:

\mu_{\hat p}=p=0.75\\\\\sigma_{\hat p}=\sqrt{\frac{p(1-p)}{n}}=\sqrt{\frac{0.75(1-0.75)}{450}}=0.0204

So, the sampling distribution of sample proportion is \hat p\sim N(0.75,0.0204^{2}).

(b)

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P(p-0.04

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(c)

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Then according to the central limit theorem the sampling distribution of sample proportion is normally distributed.

The mean and standard deviation are:

\mu_{\hat p}=p=0.75\\\\\sigma_{\hat p}=\sqrt{\frac{p(1-p)}{n}}=\sqrt{\frac{0.75(1-0.75)}{200}}=0.0306

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(d)

Compute the probability that the sample proportion will be within 0.04 of the population proportion as follows:

P(p-0.04

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Thus, the probability that the sample proportion will be within 0.04 of the population proportion is 0.81.

(e)

The probability that the sample proportion will be within 0.04 of the population proportion if the sample size is 450 is 0.95.

And the probability that the sample proportion will be within 0.04 of the population proportion if the sample size is 200 is 0.81.

So, there is a gain in precision on increasing the sample size.

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