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stich3 [128]
2 years ago
10

Suppose that many large samples are taken from a population and that the

Mathematics
1 answer:
rewona [7]2 years ago
5 0

Answer:

0.22 -1 *0.029 =0.191

0.22 +1 *0.029 =0.249

And the best option would be:

D. (0.191 to 0.249)

Step-by-step explanation:

For this case we know that the mean is:

\bar X = 0.22

And the standard error is given by:

SE = 0.029

We want to construct a 68% confidence interval so then the significance level would be :

\alpha=1-0.68 = 0.32 and \alpha/2 =0.16. The confidence interval is given by:

\bar X \pm z_{\alpha/2} SE

Now we can find the critical value using the normal standard distribution and we got looking for a quantile who accumulate 0.16 of the area on each tail and we got:

z_{\alpha/2}= 1

And replacing we got:

0.22 -1 *0.029 =0.191

0.22 +1 *0.029 =0.249

And the best option would be:

D. (0.191 to 0.249)

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A new sidewalk will be 5 feet​ wide, 100 feet​ long, and filled to a depth of 3 inches ​(0.25 ​foot) with concrete. How many cub
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Answer:

4.63 cubic yard.

Step-by-step explanation:

Given,

The length of sidewalk, l = 100 feet,

Width, w = 5 feet,

Depth, h = 3 inches = 0.25 feet,

Thus, the volume of the concrete needed for making the sidewalk,

V=l\times w\times h

=100\times 5\times 0.25

= 125 cubic feet,

∵ 1 cubic yard = 27 cubic feet,

⇒ 1 cubic feet = \frac{1}{27} cubic yard,

Thus, the quantity of concrete needed = \frac{125}{27} ≈ 4.63 cubic yard.

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Step-by-step explanation:

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The number and frequency of Atlantic hurricanes annually from 1940 through 2007 is shown here:
klio [65]

Answer:

The probability table is shown below.

A Poisson distribution can be used to approximate the model of the number of hurricanes each season.

Step-by-step explanation:

(a)

The formula to compute the probability of an event <em>E</em> is:

P(E)=\frac{Favorable\ no.\ of\ frequencies}{Total\ NO.\ of\ frequencies}

Use this formula to compute the probabilities of 0 - 8 hurricanes each season.

The table for the probabilities is shown below.

(b)

Compute the mean number of hurricanes per season as follows:

E(X)=\frac{\sum x f_{x}}{\sum f_{x}}=\frac{176}{68}=  2.5882\approx2.59

If the variable <em>X</em> follows a Poisson distribution with parameter <em>λ</em> = 7.56 then the probability function is:

P(X=x)=\frac{e^{-2.59}(2.59)^{x}}{x!} ;\ x=0, 1, 2,...

Compute the probability of <em>X</em> = 0 as follows:

P(X=0)=\frac{e^{-2.59}(2.59)^{0}}{0!} =\frac{0.075\times1}{1}=0.075

Compute the probability of <em>X</em> = 1 as follows:

\neq P(X=1)=\frac{e^{-2.59}(2.59)^{1}}{1!} =\frac{0.075\times7.56}{1}=0.1943

Compute the probabilities for the rest of the values of <em>X</em> in the similar way.

The probabilities are shown in the table.

On comparing the two probability tables, it can be seen that the Poisson distribution can be used to approximate the distribution of the number of hurricanes each season. This is because for every value of <em>X</em> the Poisson probability is approximately equal to the empirical probability.

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3 years ago
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