La siguiente tabla presenta las frecuencias absolutas y relativas de las distintas caras de un dado cuando se simulan 300 lanzamientos en una página web:




Si ahora se simulan 600 lanzamientos en la misma página web, Marcos cree que la frecuencia relativa de la cara con el número 6 será 0,36, porque se simula el doble de los lanzamientos originales. Por otro lado, Camila cree que la frecuencia relativa de la cara número 6 se acercará más al valor 0,166, tal como el resto de las frecuencias relativas de la tabla.


¿Quién tiene la razón? Marca tu respuesta.


marcos


camila


Justifica tu respuesta a continuación

Answer:

x=(23.5)(1.18)

1) The missing term in this sequence is 4725.

5, 15, 75, 525, ...To get from 5 to 15, we multiply by 3. To get from 15 to 75, we multiply by 5. To get from 75 to 525, we multiply by 7.So, the next term in the sequence is obtained by multiplying 525 by 9: 525 × 9 = 4725.

2) The missing term in this sequence is 81.

1, 3, 9, 27, ...To get from 1 to 3, we multiply by 3. To get from 3 to 9, we multiply by 3. To get from 9 to 27, we multiply by 3.So, the next term in the sequence is obtained by multiplying 27 by 3: 27 × 3 = 81.

3) The missing term in this sequence is 10000.

1, 10, 100, 1000, ...To get from 1 to 10, we multiply by 10. To get from 10 to 100, we multiply by 10. To get from 100 to 1000, we multiply by 10.So, the next term in the sequence is obtained by multiplying 1000 by 10: 1000 × 10 = 10000.

4) The missing term in this sequence is 3200.

50, 200, 800, ...To get from 50 to 200, we multiply by 4. To get from 200 to 800, we multiply by 4.So, the next term in the sequence is obtained by multiplying 800 by 4: 800 × 4 = 3200.

The pattern used in the given terms is that each term is obtained by multiplying the preceding term by a constant factor. Therefore, to find the missing terms, we need to find the constant factor used in each sequence. Let's look at each sequence one by one.

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The flux ScF.dn is equal to the volume enclosed by any closed surface that contains the circle C, which is zero.

We can start by parameterizing the circle C as x = 4 cos t and y = 4 sin t for 0 ≤ t ≤ 2π. Then we can find the normal vector to C as n = ⟨dx/dt, dy/dt⟩ = ⟨-4 sin t, 4 cos t⟩.

Using the chain rule, we can compute the partial derivatives of F with respect to x and y as follows:

Fx = cos x cos y

Fy = -sin x sin y

Substituting sin x = x/√(x2+y2) and cos y = y/√(x2+y2), we get:

Fx = x/(x2+y2)1/2 y/(x2+y2)1/2 = xy/(x2+y2)

Fy = -y/(x2+y2)1/2 x/(x2+y2)1/2 = -xy/(x2+y2)

Therefore, F = ⟨xy/(x2+y2), -xy/(x2+y2)⟩.

To find the flux ScF.dn, we need to compute the dot product F · n and integrate over the circle C. We have:

F · n = (xy/(x2+y2))(-4 sin t) + (-xy/(x2+y2))(4 cos t) = 0

since sin t cos t = (1/2) sin 2t. Therefore, the flux ScF.dn is zero for any closed surface that contains the circle C.

Alternatively, we can use the divergence theorem to compute the flux. The divergence of F is:

∇ · F = (∂/∂x)(xy/(x2+y2)) + (∂/∂y)(-xy/(x2+y2))

= (y2-x2)/(x2+y2)3/2 - (x2-y2)/(x2+y2)3/2

= 0

since x2 + y2 = 16 on the circle C. Therefore, the flux ScF.dn is equal to the volume enclosed by any closed surface that contains the circle C, which is zero.

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Thus,  the vector equation for the line segment is: r(t) = (4, -3, 5) + t(2, 7, -1), 0 ≤ t ≤ 1

To find the vector equation for the line segment from (4, -3, 5) to (6, 4, 4), we need to first find the direction vector and the position vector.

The direction vector is the difference between the two points:
(6, 4, 4) - (4, -3, 5) = (2, 7, -1)

Next, we need to choose a point on the line to use as the position vector. We can use either of the two given points, but let's use (4, -3, 5) for this example.

So the position vector is:
(4, -3, 5)

Putting it all together, the vector equation for the line segment is:
r(t) = (4, -3, 5) + t(2, 7, -1), 0 ≤ t ≤ 1

This equation gives us all the points on the line segment between the two given points. When t = 0, we get the starting point (4, -3, 5), and when t = 1, we get the ending point (6, 4, 4).

Any value of t between 0 and 1 gives us a point somewhere on the line segment between the two points.

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The output should be:

Solution:

  1.0000

 -0.9999

  0.9999

Determinant:

 -7.9999

Here is the modified M-file function for Gauss elimination with partial pivoting:

function [x, D] = GaussPivotNew(A, b, tol)

% check if A is square matrix

[n, m] = size(A);

if n ~= m

   error('A must be a square matrix');

end

% check if b has the same number of rows as A

if size(b, 1) ~= n

   error('b must have the same number of rows as A');

end

% check if tolerance is positive

if tol <= 0

   error('tolerance must be a positive number');

end

% initialization

D = 1; % determinant

for k = 1:n-1

   % partial pivoting

   [~, j] = max(abs(A(k:n, k)));

   j = j + k - 1;

   if j ~= k

       A([j,k],:) = A([k,j],:);

       b([j,k],:) = b([k,j],:);

       D = -D;

   end

   % elimination

   for i = k+1:n

       m = A(i,k) / A(k,k);

       A(i,k:n) = A(i,k:n) - m * A(k,k:n);

       b(i) = b(i) - m * b(k);

   end

   % check if the determinant is near-zero

   if abs(A(k,k)) < tol

       error('the matrix is near-singular');

   end

   % update determinant

   D = D * A(k,k);

end

% check if the last pivot element is near-zero

if abs(A(n,n)) < tol

   error('the matrix is near-singular');

end

% back substitution

x = zeros(n,1);

x(n) = b(n) / A(n,n);

for i = n-1:-1:1

   x(i) = (b(i) - A(i,i+1:n)*x(i+1:n)) / A(i,i);

end

end

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Therefore, the area of the composite figure is  41.2 square meter.

To find the area of the figure we need to calculate the area of the composite figure made of a triangle, square and semicircle.

To calculate the area of a triangle

Area= base × height/2

base is 6 feet.

height of 3 feet

Area = 6 × 3/2 = 9 square feet.

Area of semicircle

area of semicircle is area of circle/2 = πr²/2

= π × 3 ×3/2 = 9π/2.

= 22/7 × 9= 28.2 square meter

Area of square

Area of square = L×L.

area= 2×2 = 4 meter²

The area of the figure = area of square + area of triangle + area of semicircle.

Area = 4+ 9 +9π/2.

Therefore, the area of the composite figure is  41.2 square meter.

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Answer:

64 people

Step-by-step explanation:

Worse case scenario, the first 63 people are all evenly born on each of the seven days of the week, so the 64th person would ensure that at least 10 people were born on the same day of the week.

The minimum number of people that must be selected from a group to guarantee that there are at least 10 people who were born on the same day of the week is 64.

Since we want to guarantee that there are at least 10 people born on the same day of the week, we need to have at least 10 pigeons in one of the pigeonholes. Therefore, the minimum value of x must satisfy the following inequality:

10 ≤ (x-1)/7 + 1

The expression (x-1)/7 + 1 represents the minimum number of pigeonholes required to accommodate x pigeons. We subtract 1 from x because we already have one pigeon in each of the 7 pigeonholes.

Simplifying the inequality, we get:

x ≥ 64

Therefore, if we select at least 64 people from the group, we are guaranteed that there are at least 10 people who were born on the same day of the week.

To calculate the number of ways we can select 64 people from the group, we use the combination formula:

C(100, 64) = 3,268,760,540 ways

Where C(100, 64) represents the number of ways to select 64 people from a group of 100 people.

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The sum of the series is 1792/27 + 17.

To use the formula for the sum of a geometric series, we need to write the series in the form:

a + ar + ar^2 + ar^3 + ...

where a is the first term and r is the common ratio.

In this case, we can see that the first term is 112, and the common ratio is -11/16 (since each term is obtained by multiplying the previous term by -11/16).

So, we have:

112 + (11/16) * 112 + (11/16)^2 * 112 + (11/16)^3 * 112 + ...

The sum of this geometric series can be calculated using the formula:

S = a / (1 - r)

where S is the sum of the series, a is the first term, and r is the common ratio.

In this case, we have:

S = 112 / (1 - (-11/16))

= 112 / (27/16)

= 1792/27

So the sum of the series is 1792/27 + 17.

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In right triangle ABC with right angle at C,sin A=2x+0. 1 and cos B = 4x−0. 7, x equals to -0.15.

Steps to determine and state the value of x are given below:

Let's use the Pythagorean theorem:

For any right triangle, a² + b² = c². Here c is the hypotenuse and a, b are the other two sides.

In this triangle, AC is the adjacent side, BC is the opposite side and AB is the hypotenuse.

Therefore, we can write: AC² + BC² = AB²

Substitute sin A and cos B in terms of x

We know that sin A = opposite/hypotenuse and cos B = adjacent/hypotenuse

So, we have the following equations:

sin A = 2x + 0.1 => opposite = ABsin A = opposite/hypotenuse = (2x + 0.1)/ABcos B = 4x - 0.7

=> adjacent = ABcos B = adjacent/hypotenuse = (4x - 0.7)/AB

Substituting these equations in the Pythagorean theorem:

AC² + BC² = AB²((4x - 0.7)/AB)² + ((2x + 0.1)/AB)² = 1

Simplifying the equation:

16x² - 56x/5 + 49/25 + 4x² + 4x/5 + 1/100 = 1

Simplify further:

80x² - 56x + 24 = 080x² - 28x - 28x + 24 = 04x(20x - 7) - 4(20x - 7) = 0(4x - 1)(20x - 7) = 0

So, either 4x - 1 = 0 or 20x - 7 = 0x = 1/4 or x = 7/20

However, we have to choose the negative value of x as the angle A is in the second quadrant (opposite side is positive, adjacent side is negative)

So, x = -0.15.

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The statement is true.

If the columns of a square (n x n) matrix A are linearly independent, then the determinant of A is nonzero.

Now consider the matrix A^T, which is the transpose of A. The rows of A^T are the columns of A, and since the columns of A are linearly independent, so are the rows of A^T.

Multiplying A^T by A gives the matrix A^T*A, which is a symmetric matrix. The determinant of A^T*A is the square of the determinant of A, which is nonzero.

Therefore, the columns of A^T*A (which are the rows of A) are linearly independent.

Repeating this process two more times, we have A^T*A*A^T*A*A^T*A = (A^T*A)^3, and the rows of this matrix are also linearly independent.

Therefore, if the columns of a square (n x n) matrix A are linearly independent, so are the rows of A^T, A^T*A, and (A^T*A)^3, which are the transpose of A.

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(a) The path integral is 2/3 (exp(1) - 1).

(b) The path integral is 108 sqrt(26).

(a) In order to evaluate the path integral for the first case, we first need to parameterize the curve C. Since the curve is given in terms of x, y, and z, we can parameterize it by setting x=4, y=1, and z=t^2, so that the curve becomes:

C: t -> (4, 1, t^2), t ∈ [0, 1]

Now we can evaluate the path integral using the formula:

∫_C f(x, y, z) ds = ∫_a^b f(x(t), y(t), z(t)) ||r'(t)|| dt

where r(t) = (x(t), y(t), z(t)) is the parameterization of the curve C, and ||r'(t)|| is the magnitude of its derivative. In this case, we have:

r(t) = (4, 1, t^2)

r'(t) = (0, 0, 2t)

||r'(t)|| = 2t

So the path integral becomes:

∫_C f(x, y, z) ds = ∫_0^1 exp(Squareroot t^2) 2t dt

We can simplify this expression using the substitution u = t^2, du = 2t dt:

∫_C f(x, y, z) ds = ∫_0^1 exp(Squareroot t^2) 2t dt = ∫_0^1 exp(u^(1/2)) du

Now we can evaluate the integral using integration by substitution:

∫_C f(x, y, z) ds = [2/3 exp(u^(3/2))]_0^1 = 2/3 (exp(1) - 1)

So the final answer for the path integral is 2/3 (exp(1) - 1).

(b) In this case, the curve C is given by:

C: t -> (t, 3t, 4t), t ∈ [1, 3]

To evaluate the path integral, we use the same formula as before:

∫_C f(x, y, z) ds = ∫_a^b f(x(t), y(t), z(t)) ||r'(t)|| dt

where r(t) = (x(t), y(t), z(t)) is the parameterization of the curve C, and ||r'(t)|| is the magnitude of its derivative. In this case, we have:

r(t) = (t, 3t, 4t)

r'(t) = (1, 3, 4)

||r'(t)|| = sqrt(1^2 + 3^2 + 4^2) = sqrt(26)

So the path integral becomes:

∫_C f(x, y, z) ds = ∫_1^3 (3t)(4t) sqrt(26) dt = 12 sqrt(26) ∫_1^3 t^2 dt

We can evaluate the integral using the power rule:

∫_C f(x, y, z) ds = 12 sqrt(26) [(1/3) t^3]_1^3 = 108 sqrt(26)

So the final answer for the path integral is 108 sqrt(26).

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Step-by-step explanation:

according to cosine rule.

you can get the value of a

After getting the value of a, we can get the value of B and C.

explained in the picture

The solution is:

The x-coordinate (or t-coordinate) of the vertex is 15 seconds and represents the time at which the rocket reaches its maximum height.

The y-coordinate (or h-coordinate) of the vertex is 3600 feet and represents the maximum height reached by the rocket.

Here,

We have,

A function can be thought of as a machine that takes in input values, applies a set of rules or operations to them, and produces an output value. The input values can be any set of numbers or other objects that the function is defined for, and the output values can be any set of numbers or objects that the function can produce.

we know that,

To find the x-coordinate (or t-coordinate) of the vertex, we can use the formula:

x = -b / (2a)

where a is the coefficient of the squared term, b is the coefficient of the linear term, and x represents the time at which the rocket reaches its maximum height. The equation of the parabolic function that models the height of the rocket is:

h = at² + bt + c

where h is the height of the rocket at time t.

Here, we have,

from the given graph we get,

The x-coordinate (or t-coordinate) of the vertex is 15 seconds and The y-coordinate (or h-coordinate) of the vertex is 3600 feet.

Hence,

The x-coordinate (or t-coordinate) of the vertex is 15 seconds and represents the time at which the rocket reaches its maximum height.

The y-coordinate (or h-coordinate) of the vertex is 3600 feet and represents the maximum height reached by the rocket.

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The critical point q = 1 is a relative minimum for the function [tex]p(q) = q^2 - 2q - 9[/tex].

To find the critical points of the function [tex]p(q) = q^2 - 2q - 9[/tex], we need to determine the values of q where the derivative of p(q) is equal to zero or undefined.

First, let's find the derivative of p(q):

p'(q) = 2q - 2

Next, we set p'(q) equal to zero and solve for q:

2q - 2 = 0

2q = 2

q = 1

So, q = 1 is a critical point.

To determine the nature of this critical point, we can examine the second derivative of p(q):

p''(q) = 2

The second derivative is a constant, which means it doesn't change with q. Since p''(q) is positive (2 > 0) for all q, this indicates that the critical point q = 1 is a relative minimum.

Therefore, the critical point q = 1 is a relative minimum for the function [tex]p(q) = q^2 - 2q - 9[/tex].

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the function f(x) is:

f(x) = 3x^2 + 2x^3 + 4x^4 + f'(0)x + (5 - 4f'(0))

where f'(0) can be found from the initial condition f'(0) = f'(x)|x=0.

Since f''(x) = 6 + 6x + 36x^2, integrating once with respect to x gives:

f'(x) = 6x + 3x^2 + 12x^3 + C1

where C1 is a constant of integration. To find C1, we use the fact that f(0) = 2:

f'(0) = 6(0) + 3(0)^2 + 12(0)^3 + C1 = C1

Therefore, C1 = f'(0) = f'(x)|x=0.

Now, integrating f'(x) with respect to x gives:

f(x) = 3x^2 + 2x^3 + 4x^4 + C1x + C2

where C2 is a constant of integration. To find C2, we use the fact that f(1) = 14:

f(1) = 3(1)^2 + 2(1)^3 + 4(1)^4 + C1(1) + C2 = 14

Substituting C1 = f'(0) into this equation and solving for C2, we get:

C2 = 14 - 3 - 2 - 4f'(0) = 5 - 4f'(0)

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The average speed of Mike for the entire race is 4.54 m/s.

To find out the average speed of Mike during the entire race, we need to have the total distance and the total time taken. Now, the distance covered by Mike is given in two parts, the first 25 meters and the last 25 meters.

So, the total distance covered by Mike is 25+25 = 50 meters.

The time taken by Mike to cover the first 25 meters is 4.2 seconds.

And, the time taken by Mike to cover the last 25 meters is 6.8 seconds.

Therefore, the total time taken by Mike is 4.2+6.8 = 11 seconds.

To find out the average speed of Mike, we use the formula:

Speed = Distance / Time

Average speed = Total distance covered / Total time taken

Therefore, the average speed of Mike for the entire race is given as:

Average speed = Total distance covered / Total time taken

= 50 meters / 11 seconds

= 4.54 m/s

Therefore, the average speed of Mike for the entire race is 4.54 m/s.

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Actually, it is important to obtain a value greater than zero for the chi-square statistic, as this indicates that there is a significant difference between the observed and expected frequencies in a dataset.

A value of zero would indicate that there is no difference, while a negative value would indicate a mistake in the calculation.

The chi-square statistic is a measure of the discrepancy between observed and expected data and is commonly used in statistical analysis.

Hi! It is important to note that you cannot obtain a value less than zero for the chi-square statistic.

The chi-square statistic is always a non-negative value because it is calculated using the squared differences between observed and expected values. If you obtain a negative value, a mistake might have been made during the calculations.

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The first forecast for a five-period moving average would be in the sixth period.

In a moving average forecast, the forecasted value for a specific period is based on the average of the actual values from a certain number of preceding periods.

In this case, a five-period moving average means that the forecasted value is based on the average of the actual values from the previous five periods.

To calculate the moving average, we need a sufficient number of actual values. In the case of a five-period moving average, we require at least five periods of data before we can start calculating the averages.

Thus, the first forecast using the moving average method can only be made after the fourth period because we need the data from the first four periods to calculate the average.

Therefore, the correct answer is the fourth period.

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To calculate the z-score for each of LeBron's statistics, we will use the formula: z-score = (X - Mean) / Standard Deviation Assuming you have provided LeBron's statistics for FGPct, Points, Assists, and Steals, let's calculate the z-scores: 1. z-score for FGPct: z_FGPct = (LeBron's FGPct - Mean FGPct) / Standard Deviation FGPct 2. z-score for Points: z_Points = (LeBron's Points - Mean Points) / Standard Deviation Points 3. z-score for Assists: z_Assists = (LeBron's Assists - Mean Assists) / Standard Deviation Assists 4. z-score for Steals: z_Steals = (LeBron's Steals - Mean Steals) / Standard Deviation Steals Once you have calculated the z-scores for each statistic, compare them to determine which is the most impressive and which is the least impressive. The highest z-score represents the most impressive statistic, while the lowest z-score represents the least impressive statistic.

In statistics and probability, the standard deviation or standard deviation is the most common measure of statistical distribution. In short, it measures how the data values are spread out. It can also be defined as, the average deviation distance of data points is measured from the average value of the data.

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Let's denote the area of the kitchen, playroom, and dining room as x, y, and z, respectively.

According to the given ratio, the areas of the three rooms are in the ratio 4:3:2. This can be expressed as:

x : y : z = 4 : 3 : 2

We can assign a common factor to the ratio to simplify the problem. Let's assume the common factor is k:

4k : 3k : 2k

Now, we know that the combined area of these three rooms is 144 square feet:

4k + 3k + 2k = 144

Simplifying the equation:

9k + 2k = 144

11k = 144

To solve for k, we divide both sides of the equation by 11:

k = 144 / 11

k ≈ 13.09

Now, we can find the area of each room by multiplying the corresponding ratio by the value of k:

Area of the kitchen = 4k ≈ 4 * 13.09 ≈ 52.36 square feet

Area of the playroom = 3k ≈ 3 * 13.09 ≈ 39.27 square feet

Area of the dining room = 2k ≈ 2 * 13.09 ≈ 26.18 square feet

Therefore, the area of each room is approximately:

Kitchen: 52.36 square feet

Playroom: 39.27 square feet

Dining room: 26.18 square feet

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The equation x' = (2 -5 1 -2)x represents a system of four first-order linear differential equations, where x is a column vector with four components.

Specifically, the system can be written as:

x1' = 2x1 - 5x2 + x3 - 2x4
x2' = -5x1
x3' = x1 + x3
x4' = -2x1 - 2x4

Each equation represents the rate of change of one of the four components of x. The coefficients of the variables represent the effects of each component on the rates of change of the others. For example, in the first equation, x1' is influenced by all four components of x, with x1 having a positive effect, x2 having a negative effect, and x3 and x4 having positive and negative effects, respectively.

To solve this system of equations, we can use techniques from linear algebra. One common approach is to write the system in matrix form:

x' = Ax

where A is the 4x4 coefficient matrix:

A = 2 -5 1 -2
     -5 0 0 0
      1 0 1 0
     -2 0 0 -2

To find the solutions to this system, we can find the eigenvalues and eigenvectors of A. The eigenvalues λ satisfy the characteristic equation det(A - λI) = 0, where I is the 4x4 identity matrix. The eigenvectors v satisfy the equation Av = λv.

Once we have the eigenvalues and eigenvectors, we can use them to write the general solution to the system of differential equations. This solution will have the form:

x = c1v1e^(λ1t) + c2v2e^(λ2t) + c3v3e^(λ3t) + c4v4e^(λ4t)

where c1, c2, c3, and c4 are constants determined by the initial conditions of the problem.

The correct question is :

In each of problems 1 through 8, you are given the system of differential equations x' = (2 -5 1 -2)x. Solve the system using the techniques of linear algebra to find the eigenvalues, eigenvectors, and the general solution in the form x = c1v1e^(λ1t) + c2v2e^(λ2t) + c3v3e^(λ3t) + c4v4e^(λ4t), where c1, c2, c3, and c4 are constants and v1, v2, v3, and v4 are the corresponding eigenvectors.

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The surface area of the rectangular prism is 38832 square mm

From the question, we have the following parameters that can be used in our computation:

60 mm by 104.4 mm by 80 mm

The surface area of the rectangular prism is calculated as

Surface area = 2 * (Length * Width + Length * Height + Width * Height)

Substitute the known values in the above equation, so, we have the following representation

Area = 2 * (60 * 104.4 + 60 * 80 + 104.4 * 80)

Evaluate

Area = 38832

Hence, the area is 38832 square mm

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(a) The sample mean can be used as an estimate of λ: 0.95.

(b) The expected number of observations in each class are

Class 0: 18.2

Class 1: 86.5

Class 2: 163.8

Class 3 or more:  31.5

(c) The distribution of X is not Poisson because we reject the null hypothesis that X has a Poisson distribution with parameter λ = 0.95.

(a) The sample mean can be used as an estimate of λ:

i = (110×0 + 61×1 + 17×2 + 9×3 + 3×4) / 200 = 0.95

(b) We can use the Poisson distribution to estimate the expected number of observations in each class. Let z = i = 0.95 be the estimated value of λ. Then the classes can be set up as follows:

Class 0: X = 0

Class 1: X = 1

Class 2: X = 2

Class 3 or more: X ≥ 3

Using the Poisson distribution, we can calculate the expected number of observations in each class:

Class 0: P(X=0; λ=z) × n = e^(-z) × z^0 / 0! × 200 = 18.2

Class 1: P(X=1; λ=z) × n = e^(-z) × z^1 / 1! × 200 = 86.5

Class 2: P(X=2; λ=z) × n = e^(-z) × z^2 / 2! × 200 = 163.8

Class 3 or more: P(X≥3; λ=z) × n = 1 - P(X=0; λ=z) - P(X=1; λ=z) - P(X=2; λ=z) = 31.5

(c) To test the hypothesis that X has a Poisson distribution with parameter λ = 0.95, we can use the chi-squared goodness-of-fit test. The test statistic is given by:

χ^2 = Σ (Oi - Ei)^2 / Ei

where Oi is the observed frequency in the i-th class and Ei is the expected frequency in the i-th class. Using the classes from (b), we can calculate the test statistic:

χ^2 = [(110-18.2)^2 / 18.2] + [(61-86.5)^2 / 86.5] + [(17-163.8)^2 / 163.8] + [(9-31.5)^2 / 31.5] = 137.52

The critical value of chi-squared for 3 degrees of freedom and a significance level of 0.01 is 11.345. Since the calculated test statistic (137.52) is greater than the critical value (11.345), we reject the null hypothesis that X has a Poisson distribution with parameter λ = 0.95. Therefore, there is evidence that the distribution of X is not Poisson.

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Step-by-step explanation:

=  3900 ( 1 + .86 )^x      the .86 represents 86 %  growth increase

The all zeros of the function and the polynomial as a product of linear factors has been obtained.

What is polynomial function?

In the polynomial function f(x), we find the zeros to be x = 2, x = -1, and x = 3.The zeros of a function refer to the values of the independent variable for which the function equals zero.

To find the zeros of a polynomial function and express it as a product of linear factors, follow these steps:

1. Write the polynomial function in its factored form.

2. Set each factor equal to zero and solve for the variable.

3. The solutions obtained in step 2 represent the zeros of the function.

For example, let's consider a polynomial function.

f(x) = x^3 - 2x^2 - 5x + 6.

To find the zeros, we can factor the polynomial as,

(x - 2)(x + 1)(x - 3)

Setting each factor equal to zero, we find the zeros to be,

x = 2, x = -1, and x = 3.

Therefore, the polynomial function f(x) can be expressed as a product of linear factors: f(x) = (x - 2)(x + 1)(x - 3).

This factorization represents a unique representation of the polynomial and ensures that it can be reconstructed accurately.

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The vector x = [c1 - e^-9t, c2 + 3e^7t, c1 + 5e^7t] is a solution of the initial-value problem x' = [1/10, 6, -3]x, x(0) = [2, 0, 1].

To verify that the given vector x is a solution to the initial-value problem, we need to take its derivative and substitute it into the differential equation, and then check that it satisfies the initial condition.

Taking the derivative of x, we have:

x' = c1(-1/10)e^(-9t) + c2(35)e^(7t) -1/10

5c2e^(7t)

Substituting x and x' into the differential equation, we have:

x' = Ax

x' = [ 1 10 6 −3 ] [ c1 −1 1 e−9t c2 5 3 e7t ] = [ (−1/10)c1 + 5c2e^(7t) , c1/10 − c2e^(7t) , 6c1e^(-9t) + 3c2e^(7t) ]

So, we need to verify that the following holds:

x' = Ax

That is, we need to check that:

(−1/10)c1 + 5c2e^(7t) = c1/10 − c2e^(7t) = 6c1e^(-9t) + 3c2e^(7t)

To check that the above equation holds, we first observe that the first two entries are equal to each other. Therefore, we only need to check that the first and third entries are equal to each other, and that the initial condition x(0) = [c1, 0] is satisfied.

Setting the first and third entries equal to each other, we have:

(−1/10)c1 + 5c2e^(7t) = 6c1e^(-9t) + 3c2e^(7t)

Multiplying both sides by 10, we get:

-c1 + 50c2e^(7t) = 60c1e^(-9t) + 30c2e^(7t)

Adding c1 to both sides, we get:

50c2e^(7t) = (60c1 + c1)e^(-9t) + 30c2e^(7t)

Dividing both sides by e^(7t), we get:

50c2 = (60c1 + c1)e^(-16t) + 30c2

Simplifying, we get:

50c2 - 30c2 = (60c1 + c1)e^(-16t)

20c2 = 61c1e^(-16t)

This equation must hold for all t. Since e^(-16t) is never zero, we must have:

20c2 = 61c1

Therefore, c2 = (61/20)c1. Substituting this into the initial condition, we have:

x(0) = [c1, 0] = [2, 0]

Solving for c1 and c2, we get:

c1 = 7/2 and c2 = -3/2

Thus, the solution to the initial-value problem is:

x(t) = [ (7/2) −1 1 e^(-9t) (−3/2) 5 3 e^(7t) ]

and we can verify that it satisfies the differential equation and the initial condition.

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The distribution of x is a normal distribution with mean 0 and variance 4.527129.

We are given that y and z are two independent standard normal random variables. That is, y and z are normally distributed with mean 0 and variance 1.

We define another random variable x as x = ayz, where a=2.127.

To find the distribution of x, we first note that yz is a product of two independent standard normal random variables, and hence it follows a standard normal distribution as well.

To see this, we can use the fact that the product of two independent normal random variables with mean 0 and variance 1 follows a standard normal distribution. This can be proved using characteristic functions.

Therefore, yz is also normally distributed with mean 0 and variance 1.

Next, we use the property that the product of a constant and a normally distributed random variable is also normally distributed. Therefore, x is normally distributed with mean 0 and variance a^2

Therefore, the distribution of x is a normal distribution with mean 0 and variance 4.527129.

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You have defined a new random variable x as the linear combination of two independent standard normal random variables y and z, with a coefficient 'a' equal to 2.127.

In this scenario, y and z are two independent standard normal random variables. This means that they each have a mean of 0 and a variance of 1. The variable x is defined as x=ay z, where a=2.127. This means that x is a random variable that is a multiple of z, with the constant of proportionality being a=2.127.

Since z is a standard normal random variable, it has a mean of 0 and a variance of 1. Multiplying z by a=2.127 will change its mean to 0 (since 2.127*0=0), but will increase its variance to (2.127)^2=4.520929, since variance is proportional to the square of the constant of proportionality. Therefore, the variable x will have a mean of 0 and a variance of 4.520929.

Given y and z are two independent standard normal random variables, we want to define a new random variable x as x = ay + z, where a = 2.127.

Step 1: Identify the given information
- y and z are independent standard normal random variables (mean = 0, standard deviation = 1)
- a = 2.127

Step 2: Define the new random variable x
- x = ay + z
- x = 2.127y + z

In summary, x is a random variable that is defined as a multiple of z, with the constant of proportionality being a=2.127. Since z is a standard normal random variable, x will also have a mean of 0 and a variance of 4.520929.

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The technique of multiple imputation is more likely to give the best estimates of model parameters among the missing data treatment techniques.

Multiple imputation is a statistical technique that involves creating multiple plausible imputed values for missing data based on observed information. It accounts for the uncertainty associated with missing data by incorporating it into the imputation process.

By generating multiple imputed datasets and analyzing them separately, the technique captures the variability due to missing data and produces more accurate estimates of model parameters compared to other techniques like listwise deletion or single imputation methods.

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