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kotykmax [81]
3 years ago
11

The US Census Bureau reported the US population to be approximately 308,000,000 in 2010. It also reported that 6.5% of the popul

ation was under 5 years of age. How many children under the age of 5 were living in the US in 2010?
Mathematics
1 answer:
Ivahew [28]3 years ago
3 0
Given that the US population is approximately 308,000,000 in 2010 and that 6.5% of this population is under 5 years of age, we will know that number of children by multiplying 6.5% by 308,000,000. So the answer would be 20,020,000. So that is the number of children below 5 years old. Hope this helps.
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Suppose that the function f(x)=3x+10 represents the cost to rent a movie for a month. Kim has $15. How many more dollars does sh
sertanlavr [38]

Answer:

$19

Step-by-step explanation:

Given data

Cost of movie

f(x)=3x+10

Hence the cost of 8 movies is

f(x)=3(8)+10

f(x)=24+10

f(x)=$34

Hence the cost of 8 movies is $34, Kim needs extra

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5 0
3 years ago
The U.S. Bureau of Labor Statistics released hourly wage figures for various countries for workers in the manufacturing sector.
elena-14-01-66 [18.8K]

Answer:

(a) The probability that the sample average will be between $30.00 and $31.00 is 0.5539.

(b) The probability that the sample average will exceed $21.00 is 0.12924.

(c) The probability that the sample average will be less than $22.80 is 0.04006.

Step-by-step explanation:

We are given that the hourly wage was $30.67 for Switzerland, $20.20 for Japan, and $23.82 for the U.S.

Assume that in all three countries, the standard deviation of hourly labor rates is $4.00.

(a) Suppose 40 manufacturing workers are selected randomly from across Switzerland.

Let \bar X = <u>sample average wage</u>

The z score probability distribution for sample mean is given by;

                                Z  =  \frac{\bar X-\mu}{\frac{\sigma}{\sqrt{n} } } ~ N(0,1)

where, \mu = population mean wage for Switzerland = $30.67

            \sigma = standard deviation = $4.00

            n = sample of workers selected from across Switzerland = 40

Now, the probability that the sample average will be between $30.00 and $31.00 is given by = P($30.00 < \bar X < $31.00)

        P($30.00 < \bar X < $31.00) = P(\bar X < $31.00) - P(\bar X \leq $30.00)

        P(\bar X < $31) = P( \frac{\bar X-\mu}{\frac{\sigma}{\sqrt{n} } } < \frac{31-30.67}{\frac{4}{\sqrt{40} } } ) = P(Z < 0.52) = 0.69847

        P(\bar X \leq $30) = P( \frac{\bar X-\mu}{\frac{\sigma}{\sqrt{n} } } \leq \frac{30-30.67}{\frac{4}{\sqrt{40} } } ) = P(Z \leq -1.06) = 1 - P(Z < 1.06)

                                                             = 1 - 0.85543 = 0.14457

<em>The above probability is calculated by looking at the value of x = 0.52 and x = 1.06 in the z table which has an area of 0.69847 and 0.85543 respectively.</em>

Therefore, P($30.00 < \bar X < $31.00) = 0.69847 - 0.14457 = <u>0.5539</u>

<u></u>

(b) Suppose 32 manufacturing workers are selected randomly from across Japan.

Let \bar X = <u>sample average wage</u>

The z score probability distribution for sample mean is given by;

                                Z  =  \frac{\bar X-\mu}{\frac{\sigma}{\sqrt{n} } } ~ N(0,1)

where, \mu = population mean wage for Japan = $20.20

            \sigma = standard deviation = $4.00

            n = sample of workers selected from across Japan = 32

Now, the probability that the sample average will exceed $21.00 is given by = P(\bar X > $21.00)

        P(\bar X > $21) = P( \frac{\bar X-\mu}{\frac{\sigma}{\sqrt{n} } } > \frac{21-20.20}{\frac{4}{\sqrt{32} } } ) = P(Z > 1.13) = 1 - P(Z < 1.13)

                                                          = 1 - 0.87076 = <u>0.12924</u>

<em />

<em>The above probability is calculated by looking at the value of x = 1.13 in the z table which has an area of 0.87076.</em>

<em />

(c) Suppose 47 manufacturing workers are selected randomly from across United States.

Let \bar X = <u>sample average wage</u>

The z score probability distribution for sample mean is given by;

                                Z  =  \frac{\bar X-\mu}{\frac{\sigma}{\sqrt{n} } } ~ N(0,1)

where, \mu = population mean wage for United States = $23.82

            \sigma = standard deviation = $4.00

            n = sample of workers selected from across United States = 47

Now, the probability that the sample average will be less than $22.80 is given by = P(\bar X < $22.80)

  P(\bar X < $22.80) = P( \frac{\bar X-\mu}{\frac{\sigma}{\sqrt{n} } } < \frac{22.80-23.82}{\frac{4}{\sqrt{47} } } ) = P(Z < -1.75) = 1 - P(Z \leq 1.75)

                                                               = 1 - 0.95994 = <u>0.04006</u>

<em />

<em>The above probability is calculated by looking at the value of x = 1.75 in the z table which has an area of 0.95994.</em>

3 0
3 years ago
Please please help i will mark u brill <br> please fast i need it right know !!!!!
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