what is the average power that sam applies to the package to move the package from the bottom of the ramp to the top of the ramp?

The statement "Let W be the set of all vectors in R² of the form [x, y] where x and y are any real numbers with 2x + y = 0. Then W is not a subspace of R²." is false. W is indeed a subspace of R².

To show that W is a subspace of R², we need to verify three properties: closure under addition, closure under scalar multiplication, and containing the zero vector.

1. Closure under addition: Let u = [x₁, y₁] and v = [x₂, y₂] be two vectors in W. We have 2x₁ + y₁ = 0 and 2x₂ + y₂ = 0. We need to show that u + v is also in W. The sum of the vectors is u + v = [x₁ + x₂, y₁ + y₂]. By substitution, we have 2(x₁ + x₂) + (y₁ + y₂) = 2x₁ + y₁ + 2x₂ + y₂ = 0 + 0 = 0. Thus, u + v satisfies the condition 2x + y = 0, and it belongs to W.

2. Closure under scalar multiplication: Let u = [x, y] be a vector in W, and let c be any real number. We need to show that cu is also in W. The scalar multiple of the vector is cu = [cx, cy]. By substitution, we have 2(cx) + (cy) = c(2x) + c(y) = c(2x + y) = c(0) = 0. Thus, cu satisfies the condition 2x + y = 0, and it belongs to W.

3. Containing the zero vector: The zero vector [0, 0] satisfies the condition 2(0) + (0) = 0. Therefore, the zero vector is in W.

Since W satisfies all the properties of a subspace, we can conclude that W is indeed a subspace of R².

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The dot product of ū - v = (3ī + 2j - 8k) · (ī - 25 - 3k) is equal to -83.

To evaluate the dot product, we multiply the corresponding components of the two vectors and sum them up.

The given vectors are:

ū = 3ī + 2j - 8k

v = ī - 25 - 3k

Now, let's calculate the dot product:

(3ī + 2j - 8k) · (ī - 25 - 3k)

= (3 * 1) + (2 * 0) + (-8 * (-3))

(3 * 0) + (2 * (-25)) + (-8 * (-1))

(3 * (-3)) + (2 * (-0)) + (-8 * (-0))

= 3 + 0 + 24

0 - 50 + 8

9 + 0 + 0

= -83

Therefore, the dot product of ū - v is -83.

Explanation (additional details):

The dot product, also known as the scalar product, is a mathematical operation between two vectors that results in a scalar quantity. It is calculated by multiplying the corresponding components of the vectors and then summing them up.

In this case, we have two vectors: ū = 3ī + 2j - 8k and v = ī - 25 - 3k. To find their dot product, we multiply the coefficients of the same variables in each vector and add them together.

For the first component, we have (3 * 1) = 3.

For the second component, we have (2 * 0) = 0.

For the third component, we have (-8 * (-3)) = 24.

Similarly, for the remaining components:

(3 * 0) = 0, (2 * (-25)) = -50, (-8 * (-1)) = 8,

(3 * (-3)) = -9, (2 * (-0)) = 0, and (-8 * (-0)) = 0.

Adding all these products together, we get:

3 + 0 + 24 + 0 - 50 + 8 - 9 + 0 + 0 = -83.

Hence, the dot product of ū - v is -83, indicating that the two vectors are not orthogonal and have a negative scalar relationship.

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The probability of a tree's age falling within the range of 133 to 292 years is equivalent to the probability of the tree being under 292 years old, minus the probability of it being under 133 years old.

The probability that a tree's age will be under 292 years is the same as the portion of the normal distribution curve situated to the left of 292. By employing the ALEKS calculator, it was determined that the said region corresponds to a numerical value of 0. 97725

The probability that a tree will have an age less than 133 years is equal to the area under the normal distribution curve to the left of 133.

Using the ALEKS calculator, we find that this area is equal to 0.06681.

Therefore, the probability that a tree will have an age between 133 years and 292 years is equal to 0.97725 - 0.06681 = 0.91044.

To two decimal places, this is equal to 91.04%.

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Solving for a in the equation, m = (2a + t)/h, we have that a = (mh - t)/2

An equation is a mathematical expression that shows the relationship between two variables.

Given the equation m = (2a + t)/h, to solve for a, we proceed as follows

Since we have that equation  m = (2a + t)/h

First, we multiply both sides of the equation by h. So, we have that

m = (2a + t)/h

m × h= (2a + t)/h × h

mh = 2a + t

Next, we subtract t from both sides. So, we have that

mh = 2a + t

mh - t = 2a + t - t

mh - t = 2a + 0

mh - t = 2a

Finally, we divide both sides by 2. So, we have that

mh - t = 2a

(mh - t)/2 = 2a/2

(mh - t)/2 = a

So, a = (mh - t)/2

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The parametric equation of the normal line to the surface -²+4y²-422 = 11 at (3,−3,2) is:x=3t+3y=−24t−3z=2 Given equation is, -²+4y²-422 = 11.

Let's find the partial derivatives of the given surface w.r.t x, y and

z∂/∂x [-²+4y²-422]= 0∂/∂y [-²+4y²-422]

= 8y∂/∂z [-²+4y²-422]

= 0

So, the normal vector at (3,−3,2) is given by: N(3,−3,2)

=∇f(3,−3,2)=⟨0,−24,0⟩.

Tangent plane is of the form ax+by+cz+d =0.

Now, we need to find d using point (3,−3,2)3a−3b+2c+d=0

Now, we need to find a, b, and c such that they are parallel to the normal vector⟨0,−24,0⟩We know the following (z,y,z) =z i + y j + z k.

Now, we can write our tangent vector as T = ⟨1, 0, 0⟩ and ⟨0, 0, 1⟩

We take the cross-product of T and

⟨0, −24, 0⟩⟨0, −24, 0⟩ × ⟨1, 0, 0⟩ = ⟨0, 0, 24⟩⟨0, −24, 0⟩ × ⟨0, 0, 1⟩

= ⟨24, 0, 0⟩.

These are two direction vectors for the plane at (3,−3,2) and the normal vector is N(3,−3,2)=⟨0,−24,0⟩

Then the tangent plane is given by: 0(x−3)−24(y+3)+0(z−2)=00−24y−72+0=0.

Therefore, the tangent plane equation is -24y-72 = 0.

So, the parametric equations of the tangent line passing through (3,−3,2) are: x=3+0t=3y=−3−t=−3−t.

So, the parametric equation of the normal line to the surface -²+4y²-422 = 11 at (3,−3,2) is: x=3t+3y=−24t−3z=2

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The absolute maximum value of f(x, y) = x^2 + 2y^2 - x on the region R is 6, and it occurs on the boundary of the disk at the point (2, 0). The absolute minimum value of f(x, y) is 2, and it occurs on the boundary of the disk at the point (-2, 0).

To find the absolute maximum and minimum values of the function f(x, y) = x^2 + 2y^2 - x on the closed and bounded region R, which is the disk x^2 + y^2 ≤ 4, we need to evaluate the function at its critical points and on the boundary of the region.

Critical Points:

To find the critical points, we take the partial derivatives of f(x, y) with respect to x and y and set them equal to zero:

∂f/∂x = 2x - 1 = 0

∂f/∂y = 4y = 0

From the first equation, we have x = 1/2. From the second equation, we have y = 0. Therefore, the only critical point is (1/2, 0).

Boundary of the Region:

On the boundary of the disk, x^2 + y^2 = 4, we can use a parameterization to evaluate the function. Let's use x = 2cos(t) and y = 2sin(t), where t ranges from 0 to 2π.

Substituting these values into the function, we have:

f(x, y) = (2cos(t))^2 + 2(2sin(t))^2 - 2cos(t)

= 4cos^2(t) + 8sin^2(t) - 2cos(t)

= 4 - 2cos(t)

To find the maximum and minimum values of f(x, y) on the boundary, we can find the maximum and minimum values of 4 - 2cos(t) as t ranges from 0 to 2π.

The maximum value of 4 - 2cos(t) is 6, occurring at t = 0, and the minimum value is 2, occurring at t = π.

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Let's say we want to express the following expression as the sine, cosine, or tangent of a double angle. After that, we'll find the exact value of the expression.

The expression is: `tan(2pi/5)`To find the double angle, we'll use the formula:`tan 2θ = (2 tan θ)/(1 − tan^2θ)`Now let's substitute the values that we know:`tan(2pi/5) = (2 tan (pi/5))/(1 − tan^2(pi/5))

The double angle of the given expression is `pi/5`.Now let's find the exact value of the expression:`tan(pi/5) = 1.37638192047`Substituting the value in the above formula we get:`tan(2pi/5) = (2 tan (pi/5))/(1 − tan^2(pi/5)) = (2 x 1.37638192047)/(1-1.89691414861) = 2.37641486239

Therefore, the exact value of the given expression is 2.37641486239.

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sin-¹(sin(2╥/3)) = 2 pi/3.

The given expression is sin-¹(sin(2π/3)). Evaluating sin-¹(sin(2π/3)). As we know that sin-¹(sinθ) = θ for all θ ∈ [-π/2, π/2]. Now, in our expression, sin(2π/3) = sin(π/3) = sin(60°). sin 60° = √3/2, which lies in the interval [-π/2, π/2]. Therefore,   sin-¹(sin(2π/3)) = 2π/3 (in radians). Hence, the answer is 2π/3.

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We need to evaluate the definite integral [tex]\int\frac{dx}{x+6}[/tex]. The definite integral is a mathematical operation that calculates the signed area between the curve of a function and the x-axis over a given interval.

To evaluate the definite integral [tex]\int\frac{dx}{x+6}[/tex], we can apply the fundamental theorem of calculus. The integral represents the area under the curve of the function [tex]\frac{1}{x+6}[/tex] over the interval from x = 0 to x = 4.

To find the antiderivative of [tex]\frac{1}{x+6}[/tex] , we can use the natural logarithm function. Applying the logarithmic property, we can rewrite the integral as ln|x + 6| evaluated from x = 0 to x = 4. The antiderivative is ln|x + 6|.

Applying the fundamental theorem of calculus, the definite integral evaluates to ln|4 + 6| - ln|0 + 6|. Simplifying further, we get ln(10) - ln(6).

The final result of the definite integral is ln(10) - ln(6), which represents the area under the curve of the function [tex]\frac{1}{x+6}[/tex]from x = 0 to x = 4.

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The system of equations that is equivalent to the given vector equation is

x1 = -c + 3s,x2 = t  - 1.

The given vector equation is:

c = 5 + 3t + 2s

In exercise 2, the system of equations is:

x = 6 + 2t + 4s,

y = 3 + 4t + 2s,

z = 5 + 3t + 2s

In exercise 5, the given vector equation is

c = 5 + 3t + 2s

The system of equations that is equivalent to the given vector equation is:

x1 = 5c + 2s,

x2 = 3c + 4t + 3s

In exercise 6, the given vector equation is

c = -1 + t + 3s

The system of equations that is equivalent to the given vector equation is:

x1 = -c + 3s,

x2 = t  - 1.

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The flux of the vector field F = 5xi + yj + zk through the part of the surface z - 3x - 5y + 4 above the triangle in the xy-plane, oriented upward, is -132.

To set up the double integral for calculating the flux of the vector field F = 5xi + yj + zk through the part of the surface z - 3x - 5y + 4 above the triangle in the xy-plane, we need to find the normal vector to the surface.

The equation of the surface is given by z - 3x - 5y + 4 = 0.

Taking the coefficients of x, y, and z, we have the normal vector N = ( -3, -5, 1).

To calculate the flux, we need to evaluate the dot product of F and N, and then integrate over the region:

Flux = ∬ (F · N) dA

Now, let's find the limits of integration for the given triangle in the xy-plane.

The vertices of the triangle are (0,0), (0,2), and (3,0).

The x-coordinate ranges from 0 to 3, and the y-coordinate ranges from 0 to 2.

Therefore, the limits of integration are:

x: 0 to 3

y: 0 to 2

Now we can set up the double integral:

Flux = ∬ (F · N) dA = ∬ (5x(-3) + y(-5) + z(1)) dA

Since z = 3x + 5y - 4, we can substitute the value of z into the integral:

Flux = ∬ (5x(-3) + y(-5) + (3x + 5y - 4)(1)) dA

Now, we can evaluate the double integral by integrating over the given limits of integration.

Flux = ∫[0,3] ∫[0,2] (-15x - 5y + 3x + 5y - 4) dy dx

Simplifying the integral:

Flux = ∫[0,3] ∫[0,2] (-12x - 4) dy dx

Integrating with respect to y first:

Flux = ∫[0,3] [-12xy - 4y] evaluated from y = 0 to y = 2 dx

Flux = ∫[0,3] (-24x - 8) dx

Integrating with respect to x:

Flux = [-12x^2 - 8x] evaluated from x = 0 to x = 3

Flux = [(-12(3)^2 - 8(3)) - (-12(0)^2 - 8(0))]

Flux = (-108 - 24) - (0 - 0)

Flux = -132

Therefore, the flux of the vector field F = 5xi + yj + zk through the part of the surface z - 3x - 5y + 4 above the triangle in the xy-plane, oriented upward, is -132.

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Fourier series of the odd-periodic extension of the function f(x)=3, for x € (-2,0): The given function f(x) = 3 for -2 < x < 0 is an odd function with a period of 2 units.

The Fourier series of an odd function is defined as:$$f(x) = \sum_{n=1}^{\infty} b_n\sin\left(\frac{n\pi x}{L}\right)$$where $$b_n = \frac{2}{L}\int_{0}^{L} f(x)\sin\left(\frac{n\pi x}{L}\right) dx$$Since f(x) is an odd function, we have:$$b_n = \frac{2}{2}\int_{-2}^{0} 3\sin\left(\frac{n\pi x}{2}\right) dx = -\frac{12}{n\pi}[\cos(n\pi)-1]$$The Fourier series of the odd-periodic extension of the function f(x)=3, for x € (-2,0) is given as:$$f(x) = \sum_{n=1}^{\infty} -\frac{12}{n\pi}[\cos(n\pi)-1]\sin\left(\frac{n\pi x}{2}\right)$$Fourier series of the even-periodic extension of the function f(x) = 1+ 2x, for x € (0,1):The given function f(x) = 1 + 2x for 0 < x < 1 is an even function with a period of 1 unit. The Fourier series of an even function is defined as:$$f(x) = \frac{a_0}{2} + \sum_{n=1}^{\infty} a_n\cos\left(\frac{n\pi x}{L}\right)$$where $$a_0 = \frac{2}{L}\int_{0}^{L} f(x) dx$$$$a_n = \frac{2}{L}\int_{0}^{L} f(x)\cos\left(\frac{n\pi x}{L}\right) dx$$In this case, we have L = 1, hence:$$a_0 = \frac{2}{1}\int_{0}^{1} (1 + 2x) dx = 2 + 2 = 4$$$$a_n = \frac{2}{1}\int_{0}^{1} (1 + 2x)\cos(n\pi x) dx = \frac{4}{n\pi}[\sin(n\pi) - n\pi\cos(n\pi)] = \frac{4}{n\pi}[1 - (-1)^n]$$The Fourier series of the even-periodic extension of the function f(x) = 1+ 2x, for x € (0,1) is given as:$$f(x) = 2 + \sum_{n=1}^{\infty} \frac{4}{n\pi}[1 - (-1)^n]\cos(n\pi x)$$

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a) null and alternative hypotheses significance is shown; b) t = -0.96 ; c) t-value =  ±2.699 ; d) t-values =  ±2.699 ; e) we fail to reject the null hypothesis. ; f) not enough evidence to support the advertised claim.

(a) State the null and alternative hypotheses.

The null hypothesis is "There is no significant difference between the mean numbers of calls for the two shifts.

"The alternative hypothesis is "There is a significant difference between the mean numbers of calls for the two shifts."

(b) Calculate the test statistic.

The formula for calculating the test statistic is given below:

`t = (x1 - x2) / √(s12/n1 + s22/n2)`

x1 = mean number of calls per shift for Karen's shift

x2 = mean number of calls per shift for Jodi's shift

s12 = variance of the number of calls for Karen's shift (squared standard deviation)

s22 = variance of the number of calls for Jodi's shift (squared standard deviation)

n1 = sample size for Karen's shift

n2 = sample size for Jodi's shift

Substituting the given values, we get:

t = (4.2 - 4.8) / √(1.2²/25 + 1.3²/24)

t = -0.96

(c) Calculate the t-value.

The degrees of freedom can be calculated using the formula below:

`df = (s12/n1 + s22/n2)² / [(s12/n1)²/(n1-1) + (s22/n2)²/(n2-1)]`

Substituting the given values, we get:

df = (1.2²/25 + 1.3²/24)² / [(1.2²/25)²/24 + (1.3²/24)²/23]

df = 43.65

Using a t-table with 43 degrees of freedom and a significance level of 0.01, we get a t-value of ±2.699

(d) Sketch the critical region. The critical region is the shaded region.  The t-values of ±2.699.

(e) Since the calculated t-value of -0.96 does not fall within the critical region, we fail to reject the null hypothesis.

(f) We conclude that there is not enough evidence to support the advertised claim that the mean numbers of calls for the two shifts are significantly different.

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To solve the given system of equations using Gaussian elimination and back substitution, we begin by performing row operations to eliminate variables and create an upper triangular matrix.

To solve the system using Gaussian elimination, we start by performing row operations on the given system of equations. Let's label the equations as (1), (2), (3), and (4) for convenience. Our goal is to create an upper triangular matrix by eliminating variables.

In equation (2), we can replace x₂ in equations (1) and (3) to eliminate it from those equations. Equation (1) becomes -5/3x₁ + (√7/3)x₃ + 4x₄ = 6, and equation (3) becomes (√5/7)x₃ + 2x₄ = 50 - 11.

Next, we eliminate x₃ by multiplying equation (3) by -√7/√5 and adding it to equation (1). This yields -5/3x₁ + 4x₄ = 6 + (7/5)(50 - 11), which simplifies to -5/3x₁ + 4x₄ = 10.

Finally, we isolate x₄ in equation (4), which gives us x₄ = -1/2. We can substitute this value back into the previous equation to find x₁ = -5/3.

To find x₃, we substitute the values of x₁ and x₄ into equation (3), giving us (√5/7)x₃ = 50 - 11 - 2(-1/2). Simplifying further, we have (√5/7)x₃ = 55/2, and by dividing both sides by (√5/7), we find x₃ = -√5/7.

Finally, substituting the values of x₁, x₃, and x₄ into equation (2), we get 7( -5/3) + 7x₂ - √5(-√5/7) + 2(-√5/7) + 6(-√5/7) = 6. Solving this equation gives us x₂ = 3/7.

Therefore, the solution to the system of equations is x₁ = -5/3, x₂ = 3/7, x₃ = -√5/7, and x₄ = -1/2.

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Five different truck engines were used to compare the fuel economy of three different lubricating oils. Randomized complete block design is a type of experimental design used in various applications such as agriculture, industry, engineering, and medicine.

Each truck used 3 different lubricating oils (Oil 1, Oil 2, Oil 3). The mean and standard deviation of each treatment group (oil) are calculated and tabulated below. The ANOVA table for this data is presented below:Source Sum of Squares df Mean Square F P value Truck[tex]0.00166 4 0.000415 0.501 0.734 Oil 0.05834 2 0.029167 14.042 0.0005[/tex] Error 0.02966 8 0.003708 - - The treatment factor (lubricating oil) is statistically significant (p<0.05), suggesting that the lubricating oils have a significant effect on fuel consumption. However, the truck factor is not statistically significant (p>0.05). Therefore, we cannot assume any difference among the trucks with regard to fuel consumption.

Residual Analysis:The residual plot can be used to verify the assumptions of the ANOVA model. The residual plot for this experiment is presented below: The residual plot shows that the residuals are randomly distributed around zero, indicating that the assumptions of the ANOVA model are satisfied. Therefore, we can conclude that the ANOVA model is valid.

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For the polynomial of degree 3 or more, you can use the equation y = ax³ + bx² + cx + d. You can adjust the values of a, b, c, and d to create a curve that looks like the letter "K."

For the sinusoidal function, you can use the equation y = A sin(Bx + C) + D. You can adjust the values of A, B, C, and D to create a curve that looks like the letter "a."

For the rational function, you can use the equation y = (ax + b) / (cx + d). You can adjust the values of a, b, c, and d to create a curve that looks like the letter "t."

For the logarithmic function, you can use the equation y = a ln(x) + b. You can adjust the values of a and b to create a curve that looks like the letter "h."

To write the letter "Katherine J" using a polynomial of degree 3 or more, sinusoidal function, rational function, logarithmic function, and at least 3 other curves of your choice, you can follow the steps given below using Desmos.

Step 1: Open Desmos on your browser and click on the "+" icon to create a new graph.

Step 2: For the polynomial of degree 3 or more, you can use the equation y = ax³ + bx² + cx + d. You can adjust the values of a, b, c, and d to create a curve that looks like the letter "K."

Step 3: For the sinusoidal function, you can use the equation y = A sin(Bx + C) + D. You can adjust the values of A, B, C, and D to create a curve that looks like the letter "a."

Step 4: For the rational function, you can use the equation y = (ax + b) / (cx + d). You can adjust the values of a, b, c, and d to create a curve that looks like the letter "t."

Step 5: For the logarithmic function, you can use the equation y = a ln(x) + b. You can adjust the values of a and b to create a curve that looks like the letter "h."

Step 6: For the other curves of your choice, you can use any equations that you want. You can adjust the values to create curves that look like the other letters of the name.

Step 7: Adjust the domain and range of the graph to fit the letters. You can also change the colors of the curves and add a title to the graph.

Step 8: Save the graph by clicking on the "Share" button and then selecting "Copy Link." You can then paste the link in your answer or share it with your teacher as required.

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To write out the letter "Katherine J" using Desmos, we need to graph equations of different functions like polynomial, sinusoidal function, rational function, logarithmic function, and other curves. Here's how we can use each of these functions to write out the letter:

1. A polynomial of degree 3 or moreTo use a polynomial of degree 3 or more, we can use the equation of a cubic function:y = ax³ + bx² + cx + dwhere a, b, c, and d are constants that we can adjust to create the curve of the letter K. We can use the following equation to create the curve of the letter K:y = -0.1(x-1)³(x+3) + 2This will give us the curve of the letter K.

We can adjust the constants to create the curve of the other letters as well.2. A sinusoidal functionTo use a sinusoidal function, we can use the equation of a sine or cosine function:y = A sin(Bx + C) + Dwhere A, B, C, and D are constants that we can adjust to create the curve of the letter K.

We can use the following equation to create the curve of the letter K:y = -2sin(x) - 4This will give us the curve of the letter K. We can adjust the constants to create the curve of the other letters as well.3. A rational functionTo use a rational function,

we can use the equation of a function that is a ratio of two polynomials:y = (ax² + bx + c)/(dx² + ex + f)where a, b, c, d, e, and f are constants that we can adjust to create the curve of the letter K. We can use the following equation to create the curve of the letter K:y = (x² + 4)/(x² - 2x + 3)This will give us the curve of the letter K.

We can adjust the constants to create the curve of the other letters as well.4. A logarithmic functionTo use a logarithmic function, we can use the equation of a logarithmic function:y = a ln(x - b) + cwhere a, b, anareconstants that

we can adjust to create the curve of the letter K. We can use the following equation to create the curve of the letter K:y = 2 ln(x - 1) + 3This will give us the curve of the letter K.

We can adjust the constants to create the curve of the other letters as well.5. At least 3 other curves of your choiceWe can use other types of functions to create curves of the other letters. For example, we can use a quadratic function to create the curve of the letter A:y = -1.5(x - 3)² + 6We can use an exponential function to create the curve of the letter T:y = 2e^(-x/2) + 3We can use a circle function to create the curve of the letter E:(x - 3)² + (y + 3)² = 4This will give us the curve of the letter E. We can adjust the constants to create the curve of the other letters as well.Here's how all the curves look like when we put them together:

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The value (S) of the best decision alternative under Regret criteria is 27.

Regret criteria are used to minimize the amount of regret that one can feel after making a decision that ends up not working out.

Therefore, we use regret to minimize the maximum amount of regret possible. Let's calculate the regret of each alternative: Alternative 1: D1. Regret values: 0, 1, and 2.

Alternative 2: D2. Regret values: 20, 0, and 11.

Alternative 3: D3. Regret values: 29, 11, and 24. Next, we must calculate the maximum regret for each column:

Maximum regret in column 1: 29, Maximum regret in column 2: 11, Maximum regret in column 3: 24

Using the Regret Criteria, we will select the alternative with the minimum regret. Alternative 1 (D1) has a minimum regret value of 0 in column 1.

Alternative 2 (D2) has a minimum regret value of 0 in column 2. Alternative 3 (D3) has a minimum regret value of 9 in column 3.

Therefore, we select the decision alternative D2 as the best decision alternative under regret criteria since it has the lowest maximum regret among all decision alternatives.

The best decision alternative according to the regret criteria has a value (S) of 27.

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The equation of the line tangent to the curve 55 + y³ + 3x - 2y = 1 at the point (0, -1) is y = -1 - 6x.

To find the equation of the tangent line, we need to determine the slope of the curve at the given point and use the point-slope form of a line. First, we differentiate the equation of the curve with respect to x:

d/dx(55 + y³ + 3x - 2y) = d/dx(1)

3 - 2(dy/dx) + 3(dx/dx) - 2(dy/dx) = 0

6 - 4(dy/dx) = 0

dy/dx = 6/4 = 3/2

Now we have the slope of the curve at the point (0, -1). Using the point-slope form of a line, we substitute the coordinates of the point and the slope:

y - y₁ = m(x - x₁)

y - (-1) = (3/2)(x - 0)

y + 1 = (3/2)x

y = (3/2)x - 1 - 1

y = (3/2)x - 2

Therefore, the equation of the tangent line to the curve at the point (0, -1) is y = -1 - 6x.

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(a) The required error is that there is no existential or universal quantification

(b) We can consider a model that consists of three elements a, b, and c such that H(a), H(b), and G(c) are true. Then, H(c) must be false.

(a) The error in the argument is that there is no existential or universal quantification. An existential quantification states that there exists a value that satisfies the property of the argument. A universal quantification specifies that the property of the argument holds true for all the values of the variables of the argument. Hence, it should be modified by adding quantifiers to the argument. The correct argument is as follows:
`(∀x) [G(x) = H(x)]`
`(∃a) [G(a)]`
`(∃a) [H(a)]`
`(∀y) [H(y)]`

(b) In order to find the model that demonstrates the sequent `H(x)) ((x)G(x)) = ((y)H(y))`, we first translate the statement into English. The English statement is, "There is some element x for which H(x) is true, but there is no element y for which H(y) is true and G(y) is true." So, we can consider a model that consists of three elements a, b, and c such that H(a), H(b), and G(c) are true. Then, H(c) must be false.

(c) To prove `((x)G(x)) = ((y)H(y)) (3x) (G(x) = H(x))` using rules of deduction from the Predicate Calculus, we first convert the statement into an equivalent statement:

`[(∀x) G(x) → (∀y) H(y)] ∧ [(∀y) H(y) → (∀x) G(x)] ∧ (∃x) [G(x) ≠ H(x)]`

Now, we can prove the statement using the following steps:

- Step 1: `[(∀x) G(x) → (∀y) H(y)] ∧ [(∀y) H(y) → (∀x) G(x)] ∧ (∃x) [G(x) ≠ H(x)]` (Given)
- Step 2: `(∃x) [G(x) ≠ H(x)]` (Simplification of Step 1)
- Step 3: `G(a) ≠ H(a)` (Existential instantiation of Step 2)
- Step 4: `G(a) = H(a)` (3x) (G(x) = H(x)) (Universal instantiation)
- Step 5: `G(a)` (Simplification of Step 4)
- Step 6: `H(a)` (Substitution of Step 4 into Step 5)
- Step 7: `(∀y) H(y)` (Universal generalization of Step 6)
- Step 8: `[(∀x) G(x) → (∀y) H(y)]` (Simplification of Step 1)
- Step 9: `[(∀x) G(x)] → (∀y) H(y)` (Implication of Step 8)
- Step 10: `(∀y) H(y)` (Modus Ponens of Steps 5 and 9)
- Step 11: `[(∀y) H(y)] → (∀x) G(x)` (Simplification of Step 1)
- Step 12: `(∀x) G(x)` (Modus Ponens of Steps 7 and 11)
- Step 13: `((x)G(x)) = ((y)H(y))` (Biconditional introduction of Steps 9 and 11)

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The error in the following argument is in step 1 where the author makes an assumption that (3x) (G(x) = H(x)) is true, even though it has not been proved.

Therefore, the correct way would have been to use "proof by contradiction" to prove (3x) (G(x) = H(x)), that is, assume that (3x) (G(x) ≠ H(x)), then derive a contradiction.

b)To show that the following sequent cannot be proved using the Predicate Calculus, a model can be used. A model is defined as a structure of the predicates and functions in a logical formula that satisfies the given formula but does not satisfy the given sequent. Therefore, to demonstrate that the sequent H(x)) ((x)G(x)) = ((y)H(y)) cannot be proved using the Predicate Calculus, let H(x) be true, and G(x) be false for all x.

c) To prove that ((x)G(x)) = ((y)H(y)) (3x) (G(x) = H(x)), the rules of deduction from the Predicate Calculus are applied. The following is the step-by-step proof:1. (3x) (G(x) = H(x)) Assumption2. (G(a) = H(a)) a is a constant3. G(b) Assumption4. (G(b) = H(b)) 1,3, EI5. H(b) 4, MP6. (y)H(y) 5, UG7. (G(b) = H(b)) 1, UI8. (G(x) = H(x)) -> ((y)H(y)) 6, 7, Deduction Theorem9. ((x)G(x)) = ((y)H(y)) 1, 8, Deduction TheoremTherefore, ((x)G(x)) = ((y)H(y)) (3x) (G(x) = H(x)) is proved using rules of deduction from the Predicate Calculus.

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a. The 95% confidence interval of the population mean is (945.6, 967.4). b. The 90% confidence interval for the variance is [1389.44, 2488.08].

A= 21, B= 936

a) Let X be the number of issues per month. Engineers face an average of (10+B) issues per month with a standard deviation of 8. Therefore, µ = E(X) = (10 + B) and σ = Standard deviation = 8n = 36, α = 1 - 0.95 = 0.05 / 2 = 0.025 (using the normal distribution table). Thus, z0.025 = 1.96, hence the confidence interval is:
CI = (µ - z0.025(σ/√n), µ + z0.025(σ/√n))
Substitute the values in the formula,
CI = ((10 + 936) - 1.96(8/6), (10 + 936) + 1.96(8/6))
CI = (945.6, 967.4)

b) Let σ² be the variance of ages. Therefore, σ = Standard deviation = 8n = 7 + 21 = 28, α = 1 - 0.9 = 0.1 / 2 = 0.05 (using the normal distribution table).

χ²n-1, α/2 = χ²_30, 0.05 = 42.557

Substitute the values in the formula,

CI = [(n - 1) x σ² / χ² α/2, (n - 1) x σ² / χ²(1-α/2)]

CI = [(28² x 30) / 42.557, (28² x 30) / 18.493]

CI = [1389.44, 2488.08]

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The solution to this system of equations is (x, y) = (49/4, 9/4).

Given the following system of equations: 2x + 5y = 8 and 5x + 8y = 10

To solve this system of equations by elimination method, we need to multiply the first equation by 8 and second equation by -5.

So we have: 16x + 40y = 64             (1)

             -25x - 40y = -50              (2)

On adding these two equations, we have: -9x = 14   x = -14/9

Substituting x in the first equation, we have: 2(-14/9) + 5y = 8

On solving this equation, we have y = 62/45

So the solution to the given system of equations is (x, y) = (-14/9, 62/45).

To check these solutions algebraically, we substitute the values of x and y in both equations and verify if they are true or not.  

We are given another system of equations: f-5x + 9y = 13 and y=x-4We can use substitution method to solve this system.

Here, we can substitute y in the first equation with the second equation.

Hence, we get: f - 5x + 9(x - 4) = 13 Simplifying this equation, we have f - 5x + 9x - 36 = 13 Or, 4x = 49 Or, x = 49/4

Substituting x in the second equation, we have y = 49/4 - 4 Hence, y = 9/4

So, the solution to this system of equations is (x, y) = (49/4, 9/4).

Hence, the method used to solve this system is substitution method as it is simple and convenient to solve.

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The range and interquartile range of the low temperatures in Utah can be calculated based on the given data set.

The range of a data set is determined by finding the difference between the maximum and minimum values. In this case, the highest temperature is 72 and the lowest temperature is 40, so the range is 72 - 40 = 32.

The interquartile range (IQR) represents the range of the middle 50% of the data. It is calculated by finding the difference between the upper quartile (Q3) and the lower quartile (Q1). To determine Q1 and Q3, we need to find the median (Q2) first, which is the middle value of the ordered data set. After ordering the data, we find that the median is 54.

Next, we find the lower quartile (Q1), which is the median of the lower half of the data set. In this case, Q1 is 50.

Finally, we find the upper quartile (Q3), which is the median of the upper half of the data set. In this case, Q3 is 64.

The interquartile range (IQR) is then calculated as Q3 - Q1 = 64 - 50 = 14.

Both the range and the interquartile range represent measures of variability in the data set, with the range representing the overall spread and the interquartile range capturing the spread of the middle 50% of the data.


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The pie chart is attached.

To construct a pie chart illustrating the given data set, you need to calculate the percentage of each rating category based on the total number of people who sampled the breakfast bar (900).

First, let's calculate the percentage for each rating category:

Excellent: (180 / 900) x 100 = 20%

Good: (450 / 900) x 100 = 50%

Fair: (270 / 900) x 100 = 30%

Now we can create the pie chart using these percentages.

Excellent: 20% of the pie chart

Good: 50% of the pie chart

Fair: 30% of the pie chart

Hence the pie chart is attached.

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The true relative risk of the experimental medication lies between 1.17 and 3.53 with 95% certainty.

Generate a 95% confidence interval (CI) for the difference in proportions of participants reporting a reduction of symptoms between the experimental and placebo groups. The formula for the 95% confidence interval (CI) for the difference in proportions of participants reporting a reduction of symptoms between the experimental and placebo groups is given by; CI = (p1 - p2) ± 1.96 * √ [(p1 * (1 - p1) / n1) + (p2 * (1 - p2) / n2)
Where;
p1 = the proportion of participants in the experimental group that reported a reduction of symptoms
p2 = the proportion of participants in the placebo group that reported a reduction of symptoms
n1 = the number of participants in the experimental group
n2 = the number of participants in the placebo group
Substitute the values into the formula.
p1 = 38/100 = 0.38
p2 = 21/100 = 0.21
n1 = n2 = 100
CI = (0.38 - 0.21) ± 1.96 * √ [(0.38 * (1 - 0.38) / 100) + (0.21 * (1 - 0.21) / 100)]
CI = 0.17 ± 1.96 * 0.079
CI = 0.17 ± 0.155
CI = (0.015, 0.325). Hence, the 95% confidence interval (CI) for the difference in proportions of participants reporting a reduction of symptoms between the experimental and placebo groups is (0.015, 0.325).

Estimate the relative risk (RR) for reduction in symptoms between groups.
The formula for calculating the relative risk (RR) is given by;
RR = (a / (a + b)) / (c / (c + d))
Where;
a = number of participants who received the experimental medication and reported a reduction in symptoms
b = number of participants who received the experimental medication but did not report a reduction in symptoms
c = number of participants who received the placebo and reported a reduction in symptoms
d = number of participants who received the placebo but did not report a reduction in symptoms
Substitute the values into the formula.
a = 38
b = 62
c = 21
d = 79
RR = (38 / (38 + 62)) / (21 / (21 + 79))
RR = 0.38 / 0.21
RR = 1.81
Hence, the relative risk (RR) for reduction in symptoms between the experimental and placebo groups is 1.81.

Estimate the odds ratio (OR) for reduction in symptoms between groups.
The formula for calculating the odds ratio (OR) is given by;
OR = (a * d) / (b * c)
Substitute the values into the formula.
a = 38
b = 62
c = 21
d = 79
OR = (38 * 79) / (62 * 21)
OR = 1.44

Hence, the odds ratio (OR) for a reduction in symptoms between the experimental and placebo groups is 1.44. Generate a 95% confidence interval (CI) for the relative risk (RR).

The formula for calculating the standard error (SE) of the logarithm of the relative risk is given by;
SE = √ [(1 / a) - (1 / (a + b)) + (1 / c) - (1 / (c + d))]
The formula for calculating the confidence interval (CI) of the relative risk is given by; CI = e^(ln(RR) - 1.96 * SE) to e^(ln(RR) + 1.96 * SE)
Substitute the values into the formulas
SE = √ [(1 / 38) - (1 / (38 + 62)) + (1 / 21) - (1 / (21 + 79))]
SE = 0.283
CI = e^(ln(1.81) - 1.96 * 0.283) to e^(ln(1.81) + 1.96 * 0.283)
CI = 1.17 to 3.53

Hence, the 95% confidence interval (CI) for the relative risk (RR) is (1.17 to 3.53). The clinical trial was conducted to compare the effectiveness of an experimental medication to placebo in reducing the symptoms of asthma. The trial consisted of 200 participants who were randomly assigned to receive either the experimental medication or placebo. The primary outcome of the trial was a self-reported reduction of symptoms. Of the 100 participants who received the experimental medication, 38 reported a reduction in symptoms as compared to 21 participants who received the placebo. The results of the study were analyzed to generate a 95% confidence interval (CI) for the difference in proportions of participants reporting a reduction of symptoms between the experimental and placebo groups. The 95% CI was found to be (0.015, 0.325), which means that the true difference in proportions of participants reporting a reduction of symptoms between the experimental and placebo groups lies between 0.015 and 0.325 with 95% certainty. Hence, the experimental medication is statistically significant in reducing the symptoms of asthma compared to placebo. The relative risk (RR) was estimated to be 1.81, which indicates that the experimental medication is 1.81 times more effective in reducing the symptoms of asthma compared to placebo.

The odds ratio (OR) was estimated to be 1.44, which indicates that the odds of experiencing a reduction in symptoms in the experimental group were 1.44 times higher than the odds in the placebo group. A 95% CI for the relative risk (RR) was also generated, which was found to be (1.17 to 3.53). This means that the true relative risk of the experimental medication lies between 1.17 and 3.53 with 95% certainty. The clinical trial showed that the experimental medication is more effective in reducing the symptoms of asthma compared to the placebo.

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The limit as x approaches infinity of the given expression is infinity.

To find the limit as x approaches infinity of the given expression, we can analyze the highest power terms in the numerator and denominator, as they dominate the behavior of the function as x becomes large.

In the numerator, the highest power term is 3x³, and in the denominator, the highest power term is 4x². Dividing both the numerator and denominator by x², we get:

lim(x→∞) (3x³ - 6x - 2) / (4x² + x)

= lim(x→∞) (3x - 6/x² - 2/x²) / (4 + 1/x)

As x approaches infinity, the terms involving 1/x² and 1/x become negligible compared to the dominant terms of 3x and 4. Thus, the limit can be simplified to:

lim(x→∞) (3x - 0 - 0) / (4 + 0)

= lim(x→∞) (3x) / 4

Since x is approaching infinity, the numerator also approaches infinity. Hence, the limit is:

lim(x→∞) (3x) / 4 = ∞

Therefore, the limit as x approaches infinity of the given expression is infinity.

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The volume of fuel that the plane can carry is `32π/15 cubic meters`.

To find the volume of fuel that the plane can carry, we need to integrate the function f(x) from 0 to 2, which is the length of the wing.

Therefore, the volume of the fuel the plane can carry is given by:

`V = π ∫_0^2 f(x)² dx`

First, we square the function `f(x)` and simplify as follows:`f(x)² = (1/64) x^4 (2 - x)`

We can now substitute this into the integral and simplify:

`V = π ∫_0^2 (1/64) x^4 (2 - x) dx

``V = π (1/64) ∫_0^2 x^4 (2 - x) dx

``V = π (1/64) ∫_0^2 (2x^4 - x^5) dx

``V = π (1/64) [2(2/5)x^5 - (1/6)x^6]_0^2`

`V = π (1/64) [2(2/5)(32) - (1/6)(64)]

``V = 32π/15`

Therefore, the volume of fuel that the plane can carry is `32π/15 cubic meters`.

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Thus, we have shown that [tex]h(x) > 0[/tex] for all x E R, which implies that [tex]x - g(x) > 0[/tex], or equivalently, [tex]f(x) < |2x| + 1[/tex]  for all x E R. Therefore, h(x) is a non-decreasing function.

To prove that [tex]f(x) < |2| + 1[/tex] for all x E R, given that f(0) = 1 and f'(x) < 1 for all x E R, we can use the Mean Value Theorem and some properties of differentiable functions.

First, let's consider the function [tex]g(x) = |2x| + 1[/tex]. We want to show that f(x) < g(x) for all x E R.

Since f(x) is differentiable on R, it is also continuous on [0, x] for any x. By the Mean Value Theorem, there exists a number c in (0, x) such that:

[tex]f'(c) = (f(x) - f(0))/(x - 0)[/tex]

= f(x)/x

Since f'(x) < 1 for all x E R, it implies that f(x)/x < 1 for all x E R. Therefore, f(x) < x for all x E R.

Now, let's consider the function h(x) = x - g(x). We want to show that h(x) > 0 for all x E R.

[tex]h(0) = 0 - g(0) \\= 0 - (|2(0)| + 1) \\= -1 < 0[/tex]

We will prove that h(x) is a non-decreasing function. Taking the derivative of h(x), we have:

h'(x) = 1 - g'(x).

Since g'(x) = 2 for x > 0 and g'(x) = -2 for x < 0, it implies that h'(x) > 0 for x > 0 and h'(x) < 0 for x < 0.

Since h(x) is non-decreasing and h(0) < 0, it implies that h(x) > 0 for all x > 0. Similarly, h(x) > 0 for all x < 0.

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We are asked to evaluate the integral ∫[π/2, 5π/12] (3cos^(-1)(2x)/(1-sin(2x))) dx. The exact value of the integral is approximately 0.76387.

To evaluate the given integral, we first notice that the integrand involves the inverse cosine function, which means we need to find the antiderivative of this expression. Let's denote the integrand as f(x) = 3cos^(-1)(2x)/(1-sin(2x)).

Using the substitution u = 2x, we can rewrite the integral as ∫[π/4, 5π/6] (3cos^(-1)(u)/(1-sin(u))) du. Now, we need to find the antiderivative of f(u) = 3cos^(-1)(u)/(1-sin(u)) with respect to u.

To do this, we apply integration by parts, where we let u = cos^(-1)(u) and dv = du/(1-sin(u)). By differentiating u and integrating dv, we obtain du = -du/√(1-u²) and v = -ln|1 - sin(u)|.

Applying the integration by parts formula, we have ∫ f(u) du = u*(-ln|1-sin(u)|) - ∫ (-du/√(1-u²))*(-ln|1-sin(u)|) du.

After simplifying and integrating the remaining term, we obtain the antiderivative F(u) = u*(-ln|1-sin(u)|) + √(1-u²)*ln|1-sin(u)| - √(1-u²)*arcsin(u) + C.

Now, we evaluate F(u) at the limits of integration π/2 and 5π/12, which gives us F(5π/12) - F(π/2). Substituting these values into the expression, we obtain the approximate value of the integral as 0.76387.

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The difference quotient `f(x+h)-f(x)` when `h=h*0` is `-x²`. We are given the function, `f(x) = -x²+3x+8` and we need to evaluate the difference quotient `f(x+h)-f(x)` where `h = h*0`.

The difference quotient `f(x+h)-f(x)` can be evaluated by substituting the given function `f(x) = -x²+3x+8` in it.

`f(x+h)-f(x)`= `[-(x+h)²+3(x+h)+8]-[-x²+3x+8]`

= `[-(x²+2xh+h²)+3x+3h+8]+[x²-3x-8]`

= `(-x²-2xh-h²+3x+3h+8)+(x²-3x-8)`

= `-x²+2xh-h²+3h`

Here, we need to simplify the expression `-x²+2xh-h²+3h` given that `h=h*0`.When `h=0`, we have `-x²+2xh-h²+3h` = `-x²+0-0+0` = `-x²`.

Therefore, the difference quotient `f(x+h)-f(x)` when `h=h*0` is `-x²`.

f(x+h)-f(x)`= `[-(x+h)²+3(x+h)+8]-[-x²+3x+8]`

= `[-(x²+2xh+h²)+3x+3h+8]+[x²-3x-8]`

= `(-x²-2xh-h²+3x+3h+8)+(x²-3x-8)`

= `-x²+2xh-h²+3h`

When `h=0`, we have `-x²+2xh-h²+3h` = `-x²+0-0+0` = `-x²`.

Therefore, the difference quotient `f(x+h)-f(x)` calculated when `h=h*0` is `-x²`.

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The joint density function of U1 and U2 is given by, f(U1, U2) = f(U1) f(U2) if U1 > 0, U2 > 0, 0 elsewhere, f(U1, U2) = 1/α^2e^(-(U1+U2)/α) if U1 > 0, U2 > 0, 0 elsewhere and distribution function of W = U1 + U2 is F(W) = e^(-W/α), where W ≥ 0.

The probability density function of U1 is given by, f(U1) = 1/αe^(-U1/α)if U1 > 0, 0 elsewhere. The probability density function of U2 is given by, f(U2) = 1/αe^(-U2/α) if U2 > 0, 0 elsewhere. The joint density function of U1 and U2 is given by, f(U1, U2) = f(U1) f(U2) if U1 > 0, U2 > 0, 0 elsewhere, f(U1, U2) = 1/α^2e^(-(U1+U2)/α) if U1 > 0, U2 > 0, 0 elsewhere.

The distribution function of W is given by, F(W) = P(W ≤ w) = P(U1+U2 ≤ w) = ∫∫f(U1, U2) dU1 dU2Let W = U1 + U2, where U1, U2 ≥ 0. Then U2 = W - U1. Thus,∫∫f(U1, U2) dU1 dU2 = ∫∫f(U1, W - U1) dU1 d(W - U1) = ∫f(U1, W - U1) dU1 (where 0 ≤ U1 ≤ W)

The distribution function of W is given by, F(W) = ∫∫f(U1, U2) dU1 dU2 = ∫f(U1, W - U1) dU1, where 0 ≤ U1 ≤ W= ∫₀^WF(W - U1) f(U1) dU1 = ∫₀^W ∫_0^(w-u1)1/α^2e^(-(u1+u2)/α) du2du1 = ∫₀^W 1/α^2e^(-u1/α) [ ∫_0^(w-u1) e^(-u2/α) du2 ]du1= ∫₀^W 1/α^2e^(-u1/α) [ -αe^(-u2/α) ]_0^(w-u1)du1= ∫₀^W 1/αe^(-(w-u1)/α) - e^(-u1/α)du1= [ -e^(-(w-u1)/α) ]_0^W - [ -e^(-u1/α) ]_0^W= 1 - e^(-W/α) - (1 - e^(-W/α))= e^(-W/α).

Therefore, the distribution function of W = U1 + U2 is F(W) = e^(-W/α), where W ≥ 0.

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