What is the value of 5√42 to the nearest tenth?

Step-by-step explanation:

remember, a negative exponent means 1/...

so,

3^-3 = 1/3³ = 1/27

Answer:

1/27

Step-by-step explanation:

(a) The number of ways that the event can occur is 6.

(b) Probabilities are :

1) 1/2, 2) 1/6 and 3) 1/3.

(a) Given a bag of different shapes.

Total number of shapes = 6

So, if we select one shape from random,

total number of ways that the event can occur = 6

(b) Number of squares in the bag = 3

Probability of choosing a square = 3/6 = 1/2

Number of circles in the bag = 1

Probability of choosing a circle = 1/6

Number of stars in the bag = 2

Probability of choosing a star = 2/6 = 1/3

Hence the required probabilities are found.

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The volume of the cylinder is 12π units³.

Option A is the correct answer.

We have,

The formula for the volume V of a cylinder with radius r and height h is:

V = πr²h

Now,

Radius = 2 units

Height = 3 units

Now,

Volume.

= πr²h

= π x 2 x 2 x 3

= 12π units³

Thus,

The volume of the cylinder is 12π units³.

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The sphere is tangent to all 12 edges, meaning that it just touches each edge at one point without intersecting it.
First, we need to find the radius of the sphere. Since the sphere is tangent to each edge, it can be thought of as inscribed within the cube.

Drawing a diagonal of the cube creates a right triangle with legs of length 6. Using the Pythagorean theorem, we find that the length of the diagonal is $6\sqrt{3}$.
Since the sphere is inscribed within the cube, its diameter is equal to the diagonal of the cube. Therefore, the radius of the sphere is half of the diagonal, which is $\frac{1}{2}(6\sqrt{3}) = 3\sqrt{3}$.

Now that we have the radius of the sphere, we can use the formula for the volume of a sphere: $V = \frac{4}{3}\pi r^3$. Substituting in the value for the radius, we get:

$V = \frac{4}{3}\pi (3\sqrt{3})^3 \approx 113.10$

So the volume of the sphere is approximately 113.10 cubic units.
To find the volume of the sphere tangent to all 12 edges of a cube, we'll first need to determine the sphere's radius.

1. Consider the cube with edge length 6. Let's focus on one of its vertices.
2. At this vertex, there are 3 edges, each tangent to sphere S.
3. Since the sphere is tangent to all these edges, they form a right-angled triangle inside the sphere, with the edges being its legs and a diameter of the sphere being its hypotenuse.
4. Let r be the radius of sphere S.
5. Using the Pythagorean theorem, we have: (2r)^2 = 6^2 + 6^2 + 6^2
6. Simplifying, we get: 4r^2 = 108
7. Solving for r, we have: r^2 = 27, so r = √27

Now, we can find the volume of the sphere using the formula:

Volume = (4/3)πr^3

8. Substitute the value of r into the formula: Volume = (4/3)π(√27)^3
9. Simplifying, we get: Volume ≈ 36π(√27)

Thus, the volume of sphere S tangent to all 12 edges of the cube with edge length 6 is approximately 36π(√27) cubic units.

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Answer:

I believe (3, 5) and (10, 1) is the answer

Step-by-step explanation:

The probability that the subject actually has an IQ below 150 given that they are labeled as having an IQ over 150 is approximately 0.00073276, or 0.07328% when rounded to five decimal places.

Let's use Bayes' theorem to solve the problem. Let A be the event that the subject has an IQ over 150, and B be the event that the subject actually has an IQ below 150. We want to find P(B|A), the probability that the subject has an IQ below 150 given that they are labeled as having an IQ over 150.

From the problem, we know that P(A) = 1/1100, the probability that a random person has an IQ over 150. We also know that P(A|B') = 0.0008, the probability that someone with an IQ below 150 is labeled as having an IQ over 150 due to lucky guessing.

To find P(B|A), we need to find P(A|B), the probability that someone with an IQ below 150 is labeled as having an IQ over 150. We can use Bayes' theorem to find this probability:

P(A|B) = P(B|A) * P(A) / P(B)

We know that P(B) = 1 - P(B'), the probability that someone with an IQ below 150 is not labeled as having an IQ over 150. Since everyone with an IQ over 150 is labeled as such, we have:

P(B) = 1 - P(A')

where P(A') is the probability that a random person has an IQ below 150 or, equivalently, 1 - P(A).

Plugging in the given values, we have:

P(A|B) = P(B|A) * P(A) / (1 - P(A))

P(A|B) = P(B|A) * 1/1100 / (1 - 1/1100)

P(A|B) = 0.0008 * 1/1100 / (1 - 1/1100)

P(A|B) ≈ 0.00073276

Therefore, the probability that the subject actually has an IQ below 150 given that they are labeled as having an IQ over 150 is approximately 0.00073276, or 0.07328% when rounded to five decimal places.

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The value of the variable x for the arc angle m∠AB and the angle it subtends at the center of the circle C is equal to 73.

The angle subtended by an arc of a circle at it's center is twice the angle it substends anywhere on the circles circumference. Also the arc measure and the angle it subtends at the center of the circle are directly proportional.

Thus:

3x - 46 = x + 98

3x - x = 98 + 46 {collect like terms}

2x = 146

x = 146/2 {divide through by 2}

x = 73

Therefore, the value of the variable x for the arc angle m∠AB and the angle it subtends at the center of the circle C is equal to 73.

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(a) The eigenvalues of the rotation matrix T by π/3 are (1/4) + √3/4 and (1/4) - √3/4 with corresponding eigenvectors [-√3/2, 1/2] and [√3/2, 1/2].

(b) The eigenvalue 1 is always present for any rotation matrix T in R3 around an axis L, with the corresponding eigenspace being the subspace of R3 spanned by all vectors parallel to L.

(a) The matrix representation of the linear transformation T: R2 → R2, rotation by π/3 is:

T = [tex]\begin{bmatrix} \cos(\pi/3) & -\sin(\pi/3) \\ \sin(\pi/3) & \cos(\pi/3) \end{bmatrix}$[/tex]

The characteristic polynomial of T is given by:

det(T - λI) = [tex]$\begin{bmatrix} \cos(\pi/3)-\lambda & -\sin(\pi/3) \\ \sin(\pi/3) & \cos(\pi/3)-\lambda \end{bmatrix}$[/tex]

Expanding the determinant, we get:

det(T - λI) = λ² - cos(π/3)λ - sin²(π/3)

= λ² - (1/2)λ - (3/4)

Using the quadratic formula, we can solve for the eigenvalues:

λ = (1/4) ± √3/4

Therefore, the eigenvalues of T are (1/4) + √3/4 and (1/4) - √3/4.

To find the corresponding eigenvectors, we can solve the system (T - λI)x = 0 for each eigenvalue.

For λ = (1/4) + √3/4, we have:

(T - λI)x = [tex]$\begin{bmatrix} \cos(\pi/3) - (1/4+\sqrt{3}/4) & -\sin(\pi/3) \\ \sin(\pi/3) & \cos(\pi/3) - (1/4+\sqrt{3}/4) \end{bmatrix}$[/tex]

Row reducing the augmented matrix [T - λI | 0], we get:

[tex]$\begin{bmatrix} -\sqrt{3}/2 & -1/2 & | & 0 \\ 1/2 & -\sqrt{3}/2 & | & 0 \\ 0 & 0 & | & 0 \end{bmatrix}$[/tex]

Solving for the free variable, we get:

x = [tex]$t\begin{bmatrix} -\sqrt{3}/2 \\ 1/2 \end{bmatrix}$[/tex]

Therefore, the eigenvector corresponding to λ = (1/4) + √3/4 is [-√3/2, 1/2].

Similarly, for λ = (1/4) - √3/4, we have:

(T - λI)x = [cos(π/3) - (1/4 - √3/4) -sin(π/3)]

[sin(π/3) cos(π/3) - (1/4 - √3/4)]

Row reducing the augmented matrix [T - λI | 0], we get:

[tex]$\begin{bmatrix} \sqrt{3}/2 & -1/2 \\ 1/2 & \sqrt{3}/2 \end{bmatrix} \begin{bmatrix} x \ y \end{bmatrix} = \begin{bmatrix} 0 \ 0 \end{bmatrix}$[/tex]

Solving for the free variable, we get:

x = [tex]$t\begin{bmatrix} -\sqrt{3}/2 \\ 1/2 \end{bmatrix}$[/tex]

Therefore, the eigenvector corresponding to λ = (1/4) - √3/4 is [√3/2, 1/2].

(b) The axis L is an invariant subspace of T, which means that any vector parallel to L is an eigenvector of T with eigenvalue 1. This is because rotation around an axis does not change the direction of vectors parallel to the axis.

Therefore, λ = 1 is always an eigenvalue of T. The corresponding eigenspace is the subspace of R3 that is spanned by all vectors parallel to L.

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An estimated regression equation that can be used to predict a residence's average monthly gas bill for last year given its age is [tex]$\hat{y} = 115.14 - 3.167x$[/tex]. The average monthly gas bill for last year increases by $0.2456 on average.

Using age as the predictor variable and average monthly gas bill as the response variable, we can use linear regression to develop an estimated regression equation:

[tex]$\hat{y} = b_0 + b_1 x$[/tex]

where [tex]\hat{y}[/tex] is the predicted average monthly gas bill, x is the age of the residence, b₀ is the intercept and b₁ is the slope.

Using the given data, we can find the values of b₀ and b₁:

[tex]$\bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i = 17.6$[/tex]

[tex]$\bar{y} = \frac{1}{n} \sum_{i=1}^{n} y_i = 55.906$[/tex]

[tex]$s_x = \sqrt{\frac{\sum_{i=1}^{n} (x_i - \bar{x})^2}{n-1}} = 8.564$[/tex]

[tex]$s_y = \sqrt{\frac{\sum_{i=1}^{n} (y_i - \bar{y})^2}{n-1}} = 24.193$[/tex]

[tex]$r = \frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=1}^{n} (x_i - \bar{x})^2} \sqrt{\sum_{i=1}^{n} (y_i - \bar{y})^2}} = -0.577$[/tex]

[tex]$b_1 = r \frac{s_y}{s_x} = -3.167$[/tex]

[tex]$b_0 = \bar{y} - b_1 \bar{x} = 115.14$[/tex]

Therefore, the estimated regression equation is:

[tex]$\hat{y} = 115.14 - 3.167x$[/tex]

where [tex]\hat{y}$[/tex] is the predicted average monthly gas bill and x is the age of the residence.

This equation suggests that as the age of the residence increases by one year, the average monthly gas bill for last year increases by $0.2456 on average.

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Answer:a to b is 21 and b to c is 6 so I think you would need 21+6 divided by 2 i don't know for sure.

Step-by-step explanation:

The distance between the points (1, 2) and (1, -10) is 12 square units

We have to find the distance between (1, 2) and (1, -10)

The length along a line or line segment between two points on the line or line segment.

Distance=√(x₂-x₁)²+(y₂-y₁)²

=√(1-1)²+(-10-2)²

=√-12²

=√144

=12 square units

Hence,  the distance between (1, 2) and (1, -10) is 12 square units

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The mean life of the 25 items will be less than 5.9 years (rounded to one decimal place) 8% of the time.

We'll use the concepts of normal distribution, mean, standard deviation, and the z-score.

Step 1: Calculate the standard error of the mean. Standard error = (Standard deviation) / sqrt(Number of items) Standard error = 1.6 / sqrt(25) = 1.6 / 5 = 0.32 years

Step 2: Find the z-score corresponding to the 8% probability. We look for the z-score in a standard normal distribution table, which tells us that 8% of the time (0.08 probability), the z-score is approximately -1.4.

Step 3: Use the z-score formula to find the mean life (x) that corresponds to this probability. Z = (x - Mean) / Standard error -1.4 = (x - 6.3) / 0.32

Step 4: Solve for x. x - 6.3 = -1.4 * 0.32 x = 6.3 - (1.4 * 0.32) x ≈ 5.852

The mean life of the 25 items will be less than 5.9 years (rounded to one decimal place) 8% of the time.

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The researcher's fixed effects regression can provide reliable estimates of the effects of age, education, union status, and previous year's earnings on earnings if the data is accurate, the model accounts for unobservable individual characteristics, and there is no endogeneity issue between the regressors and earnings.

A fixed effects regression can provide reliable estimates of the effects of the regressors (age, education, union status, and previous year's earnings) on earnings if the following conditions are met:

1. The regressors are accurately measured, and there is enough variation in the data to capture their effects on earnings.
2. The fixed effects model accounts for all unobservable, time-invariant individual characteristics that may affect earnings. This helps control for omitted variable bias, which could otherwise lead to biased estimates.
3. There is no issue of endogeneity, such as reverse causality or simultaneity, between the regressors and the dependent variable (earnings). If this condition is not met, the estimates will be biased and inconsistent.

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The complex fourth roots of 3−33i are: [tex]z_0[/tex] = 3.062[tex]e^{(-21.603)}[/tex], [tex]z_1[/tex] = 1.513[tex]e^{(22.247)}[/tex], [tex]z_2[/tex] = 0.3826[tex]e^{(22.247)}[/tex] and [tex]z_3[/tex] = 1.198[tex]e^{(76.247)}[/tex].

To find the complex fourth roots of 3-33i, we can use the polar form of the complex number:

3-33i = 33∠(-86.41)

Then, the nth roots of this complex number are given by:

[tex]z_k[/tex] = [tex]33^{(1/n)}[/tex] × ∠((-86.41 + 360k)/n) for k = 0, 1, 2, ..., n-1

For n = 4, we have:

[tex]z_0[/tex] = [tex]33^{(1/4)}[/tex] × ∠(-86.41/4) ≈ 3.062∠(-21.603°)

[tex]z_1[/tex] = [tex]33^{(1/4)}[/tex] × ∠(88.99/4) ≈ 1.513∠(22.247°)

[tex]z_2[/tex] = [tex]33^{(1/4)}[/tex] × ∠(196.99/4) ≈ 0.3826∠(49.247°)

[tex]z_3[/tex] = [tex]33^{(1/4)}[/tex] × ∠(304.99/4) ≈ 1.198∠(76.247)

So the complex fourth roots of 3-33i are approximate:

[tex]z_0[/tex] = 3.062[tex]e^{(-21.603)}[/tex]

[tex]z_1[/tex] = 1.513[tex]e^{(22.247)}[/tex]

[tex]z_2[/tex] = 0.3826[tex]e^{(22.247)}[/tex]

[tex]z_3[/tex] = 1.198[tex]e^{(76.247)}[/tex]

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Answer:

26 units

Step-by-step explanation:

The distance between two points with coordinates

The simplified answer after Simplification of (1/2 - 1/3)(4/5 - 3/4) / (1/2 + 2/3 + 3/4) is 7/36.

To solve this expression, we need to follow the order of operations, which is parentheses, multiplication/division, and addition/subtraction.

First, we simplify the expression inside the parentheses:

(1/2 - 1/3)(4/5 - 3/4) = (1/6)(1/5) = 1/30

Next, we add up the denominators in the denominator of the entire expression:

1/2 + 2/3 + 3/4 = 6/12 + 8/12 + 9/12 = 23/12

Finally, we divide the simplified expression inside the parentheses by the fraction in the denominator:

(1/30) / (23/12) = (1/30) x (12/23) = 4/230 = 2/115 = 7/36

Therefore, the simplified answer is 7/36.

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The equivalent system of first-order differential equations for the given problem is: 1. dv1/dt = v2 2. dv2/dt = v3 3. dv3/dt = 2v2 - v1 + 1 + t*e ^t

Given differential equation: x''' - 2x'' + x' = 1 + t*e ^t

Step 1: Define new variables.
Let's introduce new variables:
v1 = x'
v2 = v1'
v3 = v2'

Now we have:
v1 = x'
v2 = v1'
v3 = v2'

Step 2: Rewrite the given equation using new variables.
Substitute the new variables into the given differential equation:
v3 - 2v2 + v1 = 1 + t*e ^t

Step 3: Write the equivalent system of first-order differential equations.
Now we have the following equivalent system of first-order differential equations:
dv1/dt = v2
dv2/dt = v3
dv3/dt = 2v2 - v1 + 1 + t*e ^t

So, the equivalent system of first-order differential equations for the given problem is:

1. dv1/dt = v2
2. dv2/dt = v3
3. dv3/dt = 2v2 - v1 + 1 + t*e ^t

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The dimension of this subspace is 4 because any invertible matrix can be written as a linear combination of the matrices:

[1 0] [0 1]

[0 0] [0 0]

[0 0] [0 0]

[0 1] [1 0]

a) Not a subspace, reason: the collection does not contain the zero matrix, which is a requirement for any subset to be a subspace.

b) Subspace, dimension = 4. This collection satisfies the three properties of being a subspace:

Contains the zero matrix: Since the determinant of the zero matrix is 0, it is not invertible. Therefore, it is not in the collection.

Closed under addition: If A and B are invertible matrices, then (A + B) is also invertible. Thus, (A + B) belongs to the collection.

Closed under scalar multiplication: If A is an invertible matrix and c is a scalar, then cA is invertible. Therefore, cA belongs to the collection.

The dimension of this subspace is 4 because any invertible matrix can be written as a linear combination of the matrices:

[1 0] [0 1]

[0 0] [0 0]

[0 0] [0 0]

[0 1] [1 0]

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The expected monthly utility bill for a 2200 square foot home in this city would be $340.

Given regression equation: U = 0.2F - 200

Where U is the monthly utility bill and F is the square footage of the residence.

Substitute F = 2200 in the equation

U = 0.2(2200) - 200

U = 440 - 200

U = $340

Hence, the expected monthly utility bill for a 2200 square foot home in this city is $340.

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The monthly utility bill would be expected for a 2200 square foot home in this city is  $240

The given regression line represents a relationship between the monthly utility bill (U) and the square footage of a residence (F) in a certain city. The equation U = 0.2F - 200 is in the form of a linear equation, where the coefficient 0.2 represents the rate of change in the utility bill for every one unit increase in square footage.

To find the expected monthly utility bill for a 2200 square foot home, we substitute F = 2200 into the equation. By plugging in this value, we can calculate the corresponding value of U, which represents the expected utility bill for that particular square footage.

Substituting F = 2200 into the equation U = 0.2F - 200, we get:

U = 0.2(2200) - 200

Calculating the expression within the parentheses gives us:

U = 440 - 200

Simplifying further:

U = 240

Therefore, the expected monthly utility bill for a 2200 square foot home in this city is $240. This means that, based on the given regression line, on average, residents with a 2200 square foot home can expect a monthly utility bill of $240 in this city.

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Answer:

option B.

Step-by-step explanation:

The original price of Casey's favorite pizza was $5.50, but now it costs $6. To find the percentage markup, we can use the formula:

(markup / original price) * 100%

The markup is the difference between the new price and the original price:

$6.00 - $5.50 = $0.50

So the markup is $0.50.

Using the formula above:

(markup / original price) * 100% = ($0.50 / $5.50) * 100% = 9.09%

Therefore, the pizza was marked up by about 9%, which is option B.

The proof that of the above expression on the condition of a/b = c/d = e/f is given below.

Given: a/b = c/d = e/f

Let e/b = e/c = k

Then, a/b = k and c/d = k, so a = kb and c = kd

Now we have:

√((a⁴ +  c⁴)/  (b⁴ + d⁴))   = √(((k b) ⁴ + ( kd )⁴ )/(b ⁴ + d ⁴)  )

= √ (k ⁴ *  (b⁴ + d⁴ ) / (b⁴ + d⁴))

= k²

Let p = 1 and q = k², then:

(p  a² + q * c²)/(p * b² + q * d²) = (a² + k² * c²)/(b² + k⁴ * d²)

= (k² *   b² + k² *    d ²)/(b ² +   k ⁴ * d ²)

= k ²

Therefore, we have shown that √ ((a ⁴ + c ⁴)/(b ⁴ + d ⁴))   = (p x a ² + q * c ²) / (p * b ² + q * d² )

if a/b =  c/ d =  e/f.

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To find the HPD (Highest Posterior Density) interval of 95% of θ, we need to first calculate the posterior distribution of θ using the Beta prior distribution with parameters α = 1 and β = 1, and the observed data of 17 satisfied and 5 not satisfied.

The posterior distribution of θ is also a Beta distribution with parameters α' = α + number of satisfied and β' = β + number of not satisfied. In this case, α' = 1 + 17 = 18 and β' = 1 + 5 = 6.

So, the posterior distribution of θ is Beta(18,6).

To find the HPD interval, we can use a numerical method such as Markov Chain Monte Carlo (MCMC) simulation. However, since the Beta distribution has a closed-form expression for the quantiles, we can use the following formula to calculate the HPD interval:

HPD interval = [Beta(q1,α',β'), Beta(q2,α',β')]

where q1 and q2 are the quantiles of the posterior distribution that enclose 95% of the area under the curve.

Using a Beta distribution calculator or software, we can find that the 0.025 and 0.975 quantiles of Beta(18,6) are approximately 0.633 and 0.898, respectively.

Therefore, the HPD interval of 95% of θ is [0.633, 0.898].

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The magnitude and direction of the vector that represents the straight path from Aaron's home to the park are approximately 8.5 km and N 34° E, respectively.

We can solve this problem by using vector addition. Let's break down Aaron's path into three vectors:

1. The first vector is 3 km at a bearing of N 30° E, which we can represent as a vector with components <2.598, 1.5>.

2. The second vector is 6 km due east, which we can represent as a vector with components <6, 0>.

3. The third vector is 4 km at a bearing of N 50° E, which we can represent as a vector with components <2.828, 3.053>.

To find the vector that represents the straight path from Aaron's home to the park, we need to add these three vectors together. We can do this by adding their components:

<2.598, 1.5> + <6, 0> + <2.828, 3.053> = <11.426, 4.553>

So the vector that represents the straight path from Aaron's home to the park has a magnitude of √(11.426² + 4.553²) = 12.3 km (rounded to the nearest tenth) and a direction of tan⁻¹(4.553/11.426) = 21° (rounded to the nearest degree) north of east.

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The requested probabilities are: A. P(X > 1) ≈ 0.628;                                  B. P(X > 0.36) ≈ 0.844; C. P(X < 0.47) ≈ 0.226;                                              D. P(0.32 < X < 2.46) ≈ 0.524

The probability density function of an exponentially distributed random variable with parameter lambda is given by:

f(x) = lambda * e^(-lambda * x), for x >= 0

The cumulative distribution function (CDF) of X is given by:

F(x) = P(X <= x) = 1 - e^(-lambda * x), for x >= 0

Using the given value of lambda = 0.47, we can solve for each probability as follows:

A. P(X > 1) = 1 - P(X <= 1) = 1 - (1 - e^(-0.47 * 1)) = e^(-0.47) ≈ 0.628

B. P(X > 0.36) = 1 - P(X <= 0.36) = 1 - (1 - e^(-0.47 * 0.36)) = e^(-0.1692) ≈ 0.844

C. P(X < 0.47) = P(X <= 0.47) = 1 - e^(-0.47 * 0.47) ≈ 0.226

D. P(0.32 < X < 2.46) = P(X <= 2.46) - P(X <= 0.32) = (1 - e^(-0.47 * 2.46)) - (1 - e^(-0.47 * 0.32)) ≈ 0.524

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There are 59,049 possible 5-digit PINs where adjacent digits cannot be identical, and you are permitted to use digits 0-9.

To determine the number of possible 5-digit PINs where adjacent digits cannot be identical and using the digits 0-9, follow these steps:

Step 1: Consider the first digit. Since there are no restrictions, you have 10 choices (0-9).

Step 2: For the second digit, you can't have it identical to the first digit. Therefore, you have 9 choices left.

Step 3: For the third digit, it can't be identical to the second digit. So, you again have 9 choices.

Step 4: Similarly, for the fourth digit, you have 9 choices.

Step 5: Finally, for the fifth digit, you have 9 choices.

Now, multiply the choices for each digit together: 10 × 9 × 9 × 9 × 9 = 59,049.

So, there are 59,049 possible 5-digit PINs where adjacent digits cannot be identical, and you are permitted to use digits 0-9.

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The likelihood of randomly selecting a terrier from the shelter would be unlikely. That is option B

The formula that can be used to determine the probability of a selected event is given as follows;

Probability = possible event/sample space.

The possible sample space for terriers = 15%

Therefore the remaining sample space goes for Labradors and Golden Retrievers which is = 75%

Therefore, the probability of selecting the terriers at random is unlikely when compared with other dogs.

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The depth of the ocean is 650 feets at the bottom of the sunlight zone.

The distance travelled by echo sound is given by the formula -

Speed = 2×distance/time

So, calculating the speed of sound from the formula using distance and time

Speed = 2×25/(1/100)

Speed = 50×1000

Speed of sound = 5000 feet/second

Now, calculating the distance or depth of ocean at the bottom of the sunlight zone -

Distance = (speed×time)/2

Distance = (5000×26/100)/2

Distance = 1300/2

Distance = 650 feets

Hence, the depth of ocean is 650 feets.

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The probability that in the next 1,000 passengers

1 No mishandled bags: 0.0295

2 Four or fewer mishandled bags: 0.3449

3 At least one mishandled bag:  0.9705.

4 At least two mishandled bags:  0.8672

1. To discover the likelihood of no misused sacks within the next 1000 travelers, able to utilize the Poisson dispersion equation:

P(X = 0) =[tex]e^(-λ) * (λ^0)[/tex] / 0!

Where λ is the anticipated number of misused sacks per 1000 travelers, which is rise to 3.52.

P(X = 0) =[tex]e^(-3.52) * (3.52^0)[/tex] / 0!

P(X = 0) = 0.0295

Hence, the likelihood of no misused sacks within the other 1000 passengers is 0.0295.

2. To discover the likelihood of  fewer misused packs within another 1000 travelers, we will utilize the total Poisson dispersion:

P(X ≤ 4) = Σ k=0 to 4 [[tex]e^(-λ) * (λ^k)[/tex]/ k! ]

P(X ≤ 4) = [[tex]e^(-3.52) * (3.52^0) / 0! ] + [ e^(-3.52) * (3.52^1) / 1! ] + [ e^(-3.52) * (3.52^2) / 2! ] + [ e^(-3.52) * (3.52^3) / 3! ] + [ e^(-3.52) * (3.52^4)[/tex]/ 4! ]

P(X ≤ 4) = 0.3449

Subsequently, the likelihood of fewer misused sacks within another 1000 travelers is 0.3449.

3. To discover the likelihood of at least one misused sack within the following 1000 travelers, able to utilize the complementary likelihood:

P(X ≥ 1) = 1 - P(X = 0)

P(X ≥ 1) = 1 - 0.0295

P(X ≥ 1) = 0.9705

Subsequently, the likelihood of at slightest one misused pack within the following 1000 passengers is 0.9705.

4. To discover the likelihood of at slightest two misused sacks within the other 1000 travelers, we can utilize the complementary likelihood once more:

P(X ≥ 2) = 1 - P(X = 0) - P(X = 1)

P(X ≥ 2) = 1 - 0.0295 - [ [tex]e^(-3.52) * (3.52^1)[/tex] / 1! ]

P(X ≥ 2) = 1 - 0.0295 - 0.1033

P(X ≥ 2) = 0.8672

Subsequently, the likelihood of at slightest two misused packs within the following 1000 travelers is 0.8672. 

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The perimeter of the square based on the dimensions of the diagonal is 145.27 millimeters.

We will begin with calculating the side of square from the diagonal of square. It will form right angled triangle and hence the formula will be represented as -

diagonal² = 2× side²

Keep the value of diagonal

(37✓2)² = 2× side²

Side² = 2638/2

Side² = 1319

Side = ✓1319

Side = 36.32 millimetres

Perimeter of the square = 4 × side

Perimeter = 145.27 millimeters

Thus, the perimeter of the square is 145.27 millimeters.

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