Please I need help on all of these expect for 1 and 2 please help I'll mark brainlisest ​

The person swims approximately   [tex]30[/tex] meters.

To find out how far the person swims, w  have to use trigonometry.the distance the person swims “d”, and let's call the angle between the person's path and the perpendicular to the river “θ”.

the sine function to relate the opposite side (which is d) to the hypotenuse (which is the distance from A to B).

sin(θ) = opposite/hypotenuse

The Pythagorean theorem can be used to determine the hypotenuse, which is the distance from A to B, and the opposite side, which is d:

distance from [tex]A to B = \sqrt(50^2 + 30^2) \approx 58.31 m[/tex]

To rewrite the equation

[tex]\sin(\theta) = d/58.31[/tex]

To solve for d, and multiply both sides by 58.31:

[tex]d = 58.31 \times \sin(\theta)[/tex]

Angle is equal to the angle formed by the individual's path and the river's perpendicular.

those tangent is equal to the ratio of the opposite side (which is 30 m, the width of the river) to the adjacent side (which is the distance from A to B along the river bank).

tan(θ) = opposite/adjacent = 30/50 = 0.6

So we can take the arctangent of 0.6 to find θ:

[tex]\theta = arctan(0.6) \approx 30.96 degrees[/tex]

Now we can substitute this value of θ into the equation we found earlier:

[tex]d = 58.31 \times sin(30.96) \approx 30 m[/tex]

Therefore, the person swims approximately 30 meters.

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Answer:

$54

Step-by-step explanation:

Find 20% of 45:

45 * .2 = 9

Add this to the original $45

45 + 9 = $54

Approximately 13.57% of the items weigh between 5.1 and 5.3 ounces.

To find the percentage of items that weigh between 5.1 and 5.3 ounces, we need to use the standard normal distribution.

First, we need to calculate the z-scores for 5.1 and 5.3 using the formula:

z = (x - μ) / σ

where x is the value we want to convert to a z-score, μ is the mean, and σ is the standard deviation.

Assuming a normal distribution with mean μ = 5 and standard deviation σ = 0.2, we can calculate the z-scores as follows:

z1 = (5.1 - 5) / 0.2 = 0.5

z2 = (5.3 - 5) / 0.2 = 1.5

Next, we use a standard normal distribution table or calculator to find the area under the curve between z1 = 0.5 and z2 = 1.5. The area represents the percentage of items that weigh between 5.1 and 5.3 ounces.

Using a standard normal distribution table or calculator, we find that the area between z1 = 0.5 and z2 = 1.5 is 0.1357 or approximately 13.57%.

Therefore, approximately 13.57% of the items weigh between 5.1 and 5.3 ounces.

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For the given series 2/3-2/5 +2/7-2/9 +2/11, it is obtained that it represents a convergent series.

What is a series?

A series in mathematics is essentially the process of adding an unlimited number of quantities, one after the other, to a specified initial amount. A significant component of calculus and its generalisation, mathematical analysis, is the study of series.

To determine whether the series is convergent or divergent, we can use the alternating series test.

The alternating series test states that if an alternating series satisfies the following two conditions, then it is convergent -

The terms of the series decrease in absolute value.

The limit of the absolute value of the terms approaches zero.

Let's check these conditions for our series -

The terms of the series are alternating and decreasing in absolute value, as can be seen by the fact that each successive term has a smaller denominator.

The limit of the absolute value of the terms is zero, since as n approaches infinity, the denominator of each term becomes arbitrarily large, while the numerator remains constant.

Therefore, the absolute value of each term approaches zero.

Since our series satisfies both conditions of the alternating series test, we can conclude that it is convergent.

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Answer:

(a) To express the quadratic function in the form of a(x - p)² + q, we first need to complete the square:

f(x) = 30x - 5x²

= -5(x² - 6x)

= -5(x² - 6x + 9 - 9)

= -5[(x - 3)² - 9]

= -5(x - 3)² + 45

Therefore, the function in the form of a(x - p)² + q is f(x) = -5(x - 3)² + 45, where a = -5, p = 3, and q = 45.

(b) The maximum height of the object occurs at the vertex of the parabola, which is at x = p = 3. Therefore, to find the maximum height, we plug x = 3 into the equation:

f(3) = -5(3 - 3)² + 45

= 45

So the maximum height of the object is 45 units.

Hope this helps!

Answer:

A

Step-by-step explanation:

"Changing the measurement unit" is the correct answer because it's like transforming a measurement into a different language or currency, but keeping the meaning or value intact. When you convert from feet to inches, you're essentially translating the measurement into a smaller unit (inches) while preserving the original quantity or value. It's similar to how you can express the same distance or length in different units, like converting from miles to kilometers or from pounds to kilograms. It's like speaking the measurement's language in a different dialect or using a different currency for the same value.

The answer is true that a 95% confidence interval will always be larger than a 90% confidence interval, all else being equal

Holding everything else constant, a 95% confidence interval will be larger than a 90% confidence interval. This is because a higher confidence level requires a wider interval to account for a larger range of possible values of the population parameter.

To clarify, a confidence interval is a range of values that is likely to contain the true value of a population parameter with a certain level of confidence. For example, a 95% confidence interval means that if the same sample were taken many times and a confidence interval calculated for each sample, then about 95% of those intervals would contain the true population parameter.

To achieve a higher level of confidence, a wider range of values needs to be considered, which results in a larger confidence interval.

So, a 95% confidence interval will always be larger than a 90% confidence interval, all else being equal.

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T is an exponential random variable with expected value 0.02 and B = { T > 0.04 }

a. Then  the conditional expected value of T given B is E[T|B] = 0.06

b. Then the conditional variance of T given B is Var[T|B] = 0.0004

We are given that T is an exponential random variable with an expected value of 0.02, and B = { T > 0.04 }.

a. To find the conditional expected value of T given B, we need to calculate E[T|B]. For an exponential random variable, the conditional expectation E[T|B] can be calculated as:

E[T|B] = E[T] + t, where t is the lower limit of the conditional range (in this case, t = 0.04).

Given the expected value of T (E[T]) is 0.02, we can plug in the values:

E[T|B] = 0.02 + 0.04 = 0.06

b. For the conditional variance of T given B, Var[T|B], it remains the same as the original variance for an exponential random variable. To find the variance, we first need to calculate the rate parameter, λ:

λ = 1 / E[T] = 1 / 0.02 = 50

The variance of an exponential random variable is given by Var[T] = 1 / λ²:

Var[T|B] = Var[T] = 1 / (50²) = 1 / 2500 = 0.0004

Your answer:
a. E[T|B] = 0.06
b. Var[T|B] = 0.0004

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Therefore, there will be approximately 20,480 bacteria after 30 minutes. The correct answer is (b) 20,480 bacteria.

In this experiment, we have an initial bacterial count of 5,000 at time 0 and a count of 8,000 at 10 minutes. Since the growth is exponential, we can use the exponential growth formula:

[tex]N(t) = N₀ * (1 + r)^t[/tex]

Where N(t) is the number of bacteria at time t, N₀ is the initial number of bacteria, r is the growth rate, and t is the time.

First, let's find the growth rate using the data provided:

[tex]8000 = 5000 * (1 + r)^{10\\\\(1 + r)^{10} = 8000 / 5000 = 1.6[/tex]

Now, let's find the 10th root of 1.6 to find the growth rate (1 + r):

[tex]1 + r = 1.6^{1/10} =1.0481[/tex]

So, the growth rate (r) is approximately 0.0481.

Next, we want to find the number of bacteria after 30 minutes:

N(30) = 5000 * (1.0481)^30 ≈ 20480.18

Therefore, there will be approximately 20,480 bacteria after 30 minutes. The correct answer is (b) 20,480 bacteria.

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Answer:

Step-by-step explanation:

bbg

A higher confidence level provides greater certainty while a lower confidence level provides less certainty

If a 99% confidence interval for a slope in a regression model is wider than the corresponding 95% confidence interval.

It means that we are more confident in the estimate of the slope with the 99% interval, but this confidence comes at the cost of a wider range of plausible values.

In other words, with the 99% confidence interval, we are more certain that the true value of the slope lies within the interval, but the interval is wider and hence provides less precision than the 95% interval.

This is because to be more certain that the interval contains the true slope, we need to include a wider range of plausible values.

It is important to note that the choice of the confidence level depends on the trade-off between the level of certainty and the level of precision desired for the estimate.

A higher confidence level provides greater certainty but at the cost of wider intervals and less precision, while a lower confidence level provides less certainty but narrower intervals and greater precision.

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Answer:

Step-by-step explanation:

To compare which option is the better deal, we need to determine the final price of the sofa after applying each discount.

For Choice A, the price after the 15% discount is:

1250 - (0.15 x 1250) = 1250 - 187.50 = $1062.50

For Choice B, the price after the $200 rebate is:

1250 - 200 = $1050

Therefore, Choice B, with a $200 rebate, is the better deal for the customer as it results in a lower final price of $1050, compared to Choice A, which results in a final price of $1062.50 after the 15% discount.

The total number of n-digit numbers with all digits odd, such that 1 and 3 each occur a nonzero, even number of times is  5^n * (n+1)^3 + 2 * 5^(n-1) * n * (n-2)^2.

Let's first consider the odd digits that can appear in an n-digit number, which are 1, 3, 5, 7, and 9. Since each digit can be used independently of the others, the total number of possible n-digit numbers with all digits odd is simply 5^n.

Now let's consider the condition that 1 and 3 each occur a nonzero, even number of times. There are two possibilities either both digits occur an even number of times (0, 2, 4, 6, or 8), or both digits occur an odd number of times (1, 3, 5, 7, or 9). We will consider each case separately.

Both 1 and 3 occur an even number of times.

In this case, we can choose the number of times each of the other three digits appears independently of each other.

Each of the other three digits can appear 0, 1, 2, 3, ..., or n times. Thus, the total number of n-digit numbers with all digits odd and with both 1 and 3 appearing an even number of times is

5^n * (n+1)^3

This is because there are (n+1) choices for each of the three remaining odd digits (since each can appear 0, 1, 2, ..., or n times), and we have 3 remaining digits to choose.

Both 1 and 3 occur an odd number of times.

In this case, we must choose one of the digits (1 or 3) to appear once, and the other to appear three times. We can choose which digit appears once in 2 ways. We then have (n-1) remaining digits to choose, and we must choose one of the remaining digits to appear 1, 3, 5, ..., or (n-1) times.

The other two remaining digits can appear 0, 2, 4, ..., or (n-2) times each. Thus, the total number of n-digit numbers with all digits odd and with both 1 and 3 appearing an odd number of times is

2 * 5^(n-1) * n * (n-2)^2

This is because we have 2 choices for which digit appears once, 5 choices for the once-appearing digit, n-1 choices for the location of the once-appearing digit, n choices for the digit that appears three times, and (n-2) choices for each of the other two digits (since each can appear 0, 2, 4, ..., or (n-2) times).

Thus, the total number of n-digit numbers is

5^n * (n+1)^3 + 2 * 5^(n-1) * n * (n-2)^2

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A percent histogram using classes of interval width 10% starts at 0%, is present in above figure 4. So, option(a) is right one. Approx. 40%, of population being a minority between the ages of 18 and 34.

In 1980, percentage of minority of adults aged between 18 to 34 = 20% . By the end of 2013, percentage of minority of adults aged between 18 to 34 is more than doubled that is > 40%. Observational data consists minority data of 50 states in a country.

A histogram is a type of graphical representation of data. It is used to represent the frequency distribution of a data points of one variable. Histograms is classify data into various bars or range groups, showing the number of observations that fall within each bar. So, steps to draw the histogram,

Figure 4 shows the percent histogram for a group starting at 0% with a width of 10%. Therefore, the correct answer is option one. Percentage of minorities aged 18 to 34 showing that they are not minorities, which corresponds to about 40% of the population. Therefore, the histogram slopes slightly to the right.

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Complete question:

The above table complete question.

Hey I really need help. How do I make a histogram with this information??

APPLY YOUR KNOWLEDGE 1. 6 The Changing Fate of America. In 1980, approximately 20% of adults aged 18–34 were considered minorities, reporting their ethnicity as other than non- Hispanic white. By the end of 2013, that percentage had more than doubled. How are minorities between the ages of 18 and 34 distributed in the United States? In the country as a whole, 42. 8% of adults aged 18–34 are considered minorities, but the states vary from 8% in Maine and Vermont to 75% in Hawaii. Table 1. 2 presents the data for all 50 states and the District of Columbia. Make a histogram of the percents using classes of width 10% starting at 0%. That is, the first bar covers 0% to < 10%, the second covers 10% to < 20%, and so on. (Make this histogram by hand, even if you have software, to be sure you understand the process. You may then want to compare your histogram with your software's choice. ) options present in above figure 2.

Answer:

The number of tigers is reduced by a factor of 1/3 every 2 years.

Step-by-step explanation:

1) 4n⁶+20n⁵

4n⁵(n+5)

2) 49n²+63n³

7n²(7+9n)

The location of the image is -28 cm (behind the mirror), and the magnification of the image is 0.093.

The mirror equation is:

1/f = 1/o + 1/i

where f is the focal length of the mirror, o is the distance of the object from the mirror, and i is the distance of the image from the mirror.

Plugging in the given values, we get:

1/0.85 = 1/3.0 + 1/i

Solving for i, we get:

i = 1 / (1/f - 1/o)

i = 1 / (1/0.85 - 1/3.0)

i = -0.28 m

The negative sign indicates that the image is virtual and located behind the mirror. Therefore, the image is located 28 cm behind the mirror.

Now, let's use the magnification equation:

m = -i / o

Plugging in the values we get:

m = -(-0.28 m) / 3.0 m

m = 0.093

Therefore, the magnification of the image produced by the mirror is 0.093.

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The general formula for given alternating series is ∑n=1[[tex]\infty[/tex]]([tex](-1)^(^n^+^1^) * [(50 + 2n)/(51 + 2n)][/tex])

The alternating series can be written in the form ∑n=1[[tex]\infty[/tex]]an, where an is the nth term of the series. To find the general formula for the series, we need to first identify the pattern in the terms.

We can see that the terms of the series alternate in sign and that the numerator and denominator of each term differ by 1. Therefore, we can write the general formula for the nth term of the series as:

aₙ = [tex](-1)^(^n^+^1^) * [(50 + 2n)/(51 + 2n)][/tex]

Using this formula, we can find the first few terms of the series and check if they match the given series:

a₁ = [tex](-1)^(^1^+^1^) * [(50 + 21)/(51 + 21)] = 2/53[/tex]

a₂ = [tex](-1)^(^2^+^1^) * [(50 + 22)/(51 + 22)] = -4/55[/tex]

a₃ = [tex](-1)^(^3^+^1^) * [(50 + 23)/(51 + 23)] = 6/57[/tex]

Therefore, the general formula for the alternating series ∑n=1[[tex]\infty[/tex]](52−53, 54−55, ⋯) in the form of ∑n=1[[tex]\infty[/tex]]an is:

∑n=1[[tex]\infty[/tex]]([tex](-1)^(^n^+^1^) * [(50 + 2n)/(51 + 2n)][/tex])

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Answer:

I think it's 66,6666666665

Step-by-step explanation:

/5 = is the total of strawberries = 100

/6 = is the total of strawberries (ate) = 80

Arianna :

100 ÷ 5 = 20 × 4 = 80

Arianna's Sister :

5/6 out of 80 [4/5 !] = 80 ÷ 6 = 13,3333333333 × 5 = 66,6666666665

Hope I helped you ! :>

The answer is The data is skewed, with a range of 9. This might happen because the bookstore gives away a free tote bag when you buy a book over $7.

Line plot is used to show frequency of data within a given range. Line plots consist of a single line that connects individual data points, showing the frequency of their occurrence.

The line plot suggests that the majority of the books are priced at $3, but there are also a few more expensive books.

This is evidenced by the fact that the dots follow a pattern of two over two, three over three, four over four, three over five and two over six.

This means that the data is skewed, because the majority of the books are cheaper, but there are still a few more expensive books available. The range of the data is 9, indicating that the bookstore is giving away a free tote bag when you buy a book over $7.

This explains why the data is skewed, as the free tote bag incentivizes people to buy the more expensive books.

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We have derived the desired expressions: E[Y] = μE[N], Var(Y) = σ²E[N] + μ²Var[N]

To answer your question, let's start by defining the given terms:

N = number of shops visited (positive integer random variable)
Xi = amount of money spent in the i-th shop
Y = X1 + X2 + ... + XN (total amount of money spent)
μ = mean of the Xi's
σ² = variance of the Xi's

We are given that N and all Xi are independent, and we want to show that:

E[Y] = μE[N]
Var(Y) = σ²E[N] + μ²Var[N]

First, let's find E[Y]:

E[Y] = E[X1 + X2 + ... + XN]
By linearity of expectation:
E[Y] = E[X1] + E[X2] + ... + E[XN]

Since all Xi's have the same mean (μ), we can rewrite this as:

E[Y] = Nμ
Since N is a random variable, we take the expectation of N:
E[Y] = μE[N]

Next, let's find Var(Y):

Var(Y) = Var(X1 + X2 + ... + XN)

As N and all Xi are independent, we can say:

Var(Y) = Var(X1) + Var(X2) + ... + Var(XN)

Since all Xi's have the same variance (σ²), we can rewrite this as:

Var(Y) = Nσ²

Now, we need to find the variance of N:

Var(Y) = σ²E[N] + μ²Var[N]

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We have found that B can be expressed in terms of A as B = (P*A)*P.

We are given that A = PBP^(-1) and we need to solve for B in terms of A. To do this, we'll follow these steps:

Step 1: Multiply both sides of the equation by P.
P*A = P*(PBP^(-1))

Step 2: Use the property of matrix multiplication (AB)C = A(BC) to rearrange the terms.
P*A = (PP)B(P^(-1))

Step 3: Since P is invertible, PP^(-1) = I (Identity matrix), so we can simplify the equation.
P*A = I*B*P^(-1)

Step 4: As the identity matrix (I) has no effect on the product when multiplied, we can further simplify the equation.
P*A = B*P^(-1)

Step 5: Multiply both sides of the equation by the inverse of P^(-1), which is P.
(P*A)*P = B*(P^(-1)*P)

Step 6: Use the property of matrix multiplication again to rearrange the terms.
(P*A)*P = B*(P^(-1)*P)

Step 7: As P^(-1)*P = I (Identity matrix), we can simplify the equation.
(P*A)*P = B*I

Step 8: Since the identity matrix (I) has no effect on the product when multiplied, we get the final equation for B.
B = (P*A)*P
So, we have found that B can be expressed in terms of A as B = (P*A)*P.

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The linear regression equation is y = -1.16x + 84.95. The correlation coefficient is approximately -0.89.

Correlation coefficient is a statistical measure that shows the strength of the relationship between two variables. It is represented by the symbol "r" and its value ranges between -1 to +1.

A value of -1 indicates a perfect negative correlation, meaning that as one variable increases, the other variable decreases.

A value of +1 indicates a perfect positive correlation, meaning that as one variable increases, the other variable also increases.

A value of 0 indicates no correlation between the two variables, meaning that there is no relationship between them.

The magnitude of the correlation coefficient indicates the strength of the relationship. A value close to -1 or +1 indicates a strong correlation, while a value close to 0 indicates a weak correlation.

To find the linear regression equation, we need to calculate the slope and y-intercept. We can use the formula:

slope (b) = (n∑xy - ∑x ∑y) / (n∑x² - (∑x)²)

y-intercept (a) = (∑y - b∑x) / n

where n is the number of data points.

Using the given data, we have:

n = 9

∑x = 99

∑y = 601

∑xy = 12032

∑x² = 1168

slope (b) = (9(12032) - (99)(601)) / (9(1168) - (99)²) ≈ -1.16

y-intercept (a) = (601 - (-1.16)(99)) / 9 ≈ 84.95

Therefore, the linear regression equation is:

y = -1.16x + 84.95

The correlation coefficient can be calculated using the formula:

r = [n∑xy - (∑x)(∑y)] / √[(n∑x² - (∑x)²)(n∑y² - (∑y)²)]

Using the given data, we have:

r = [9(12032) - (99)(601)] / √[(9(1168) - (99)²)(9(29696) - (601)²)] ≈ -0.89

The correlation coefficient is approximately -0.89. This indicates a strong negative correlation between the number of classes missed and the final exam score. In other words, as the number of classes missed increases, the final exam score tends to decrease. The linear fit of the data is good, as indicated by the strong correlation coefficient.

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The statement "gateway of last resort is not set" means that there is no default route configured in a routing table of a network device (such as a router).

A default route, also known as the gateway of last resort, is used when there is no specific route available for a destination IP address, and thus, the network device needs a general path to forward the traffic. When the gateway of last resort is not set, the device will not know where to send traffic for unknown destinations, which may cause connectivity issues.

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The ratio of slit width to slit separation for this grating is n/(n+1).

The ratio of slit width to slit separation for this grating can be calculated using the equation:

d sinθ = mλ

where d is the slit separation, θ is the diffraction angle, m is the interference order, and λ is the wavelength of light.

Since the mth interference order is missing, we can assume that m = n + 1, where n is the order of the nth diffraction minimum.

For the nth diffraction minimum, we know that:

sinθ = nλ/d

Substituting m = n + 1 into the interference equation, we get:

d sinθ = (n + 1)λ

d (nλ/d) = (n + 1)λ

Canceling out λ and simplifying, we get:

d/n = (n + 1)/m

Since we are looking for the ratio of slit width to slit separation, we can express d/n as w, where w is the slit width. Similarly, we can express (n + 1)/m as s, where s is the slit separation. Thus, we have:

w/s = (n/n+1)

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Answer:

8.3[tex]cm^{3}[/tex]

Step-by-step explanation:

Answer:

Well, if you picked seven numbers, then at most you could pick seven even numbers.

At least you could pick zero.

Step-by-step explanation:

I feel like Im reading this wrong, but its true for the question you asked. Sorry if its wrong qwq

The required answer is P(X=13)≈ 0.01353

To solve this problem, we can use the Poisson distribution formula:

P(X=k) = (e^(-λ) * λ^k) / k!

Where X is the number of requests, λ is the average rate (α multiplied by the time period, which is 4*5=20), and k is the number of requests we want to find the probability for (in this case, k=13).
These concepts have been given an axiomatic mathematical formalization in probability theory, a branch of mathematics that is used in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science and game theory to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems
So, substituting the values:

P(X=13) = (e^(-20) * 20^13) / 13!

= 0.088 (rounded to three decimal places)
Therefore, the probability that exactly thirteen requests are received during a particular 5-hour period is 0.088.

These concepts have been given an axiomatic mathematical formalization in probability theory, a branch of mathematics that is used in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science and game theory to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems

Step 1: Calculate the average number of requests in the 5-hour period.
λ = α * time period = 4 requests/hour * 5 hours = 20 requests

Step 2: Use the Poisson probability formula.
P(X=k) = (e^(-λ) * (λ^k)) / k!, where X is the number of requests, k is the desired number of requests (13 in this case), λ is the average number of requests in the 5-hour period, and e is the base of the natural logarithm (approximately 2.71828).

Step 3: Plug in the values into the formula.
P(X=13) = (e^(-20) * (20^13)) / 13!

Step 4: Calculate the probability.
P(X=13) ≈ (2.06 * 10^(-9) * 4.10 * 10^(18)) / 6,227,020,800 ≈ 0.01353

So, the probability that exactly 13 requests are received during a particular 5-hour period is approximately 0.014 (rounded to three decimal places).

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