Which of the following is the function for the graph below?

The value of the expression is 1/100.

We have,

To solve this expression, we can use the division property of fractions which states that dividing by a fraction is the same as multiplying by its reciprocal.

So, we have:

1/20 ÷ 5

= 1/20 x 1/5  (reciprocal of 5 is 1/5)

= 1/100   (multiply numerator with numerator and denominator with denominator)

Therefore,

The simplified result is 1/100.

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The television researcher watched the Simpsons for 3 and 1/4 hours.

To find the number of hours the researcher watched the Simpsons, we need to use the given information that Bart makes a bad decision every 1/4 of an hour. This means that in one hour (or 4/4 of an hour), Bart makes 4 bad decisions.

To find how many hours the researcher watched, we can divide the number of bad decisions by 4:

13 bad decisions ÷ 4 bad decisions per hour = 3.25 hours

Therefore, the researcher watched the Simpsons for 3 and 1/4 hours.

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It would take around 8 hours to bring each lake's pollution level down to 50% of its starting point, and around 22 hours to bring it down to 5%.

Assuming each lake is a separate tank with only clean water flowing in, we can use the exponential decay model [tex]$A = A_0e^{-kt}$[/tex], where $A$ is the amount of pollutant at time t, A₀ is the initial amount of pollutant, and k is the decay constant.

To find the time it would take to reduce the pollution level in each lake to 50% of its original level, we need to solve the equation [tex]$0.5A_0 = A_0e^{-kt}$[/tex] for t:

[tex]0.5A_0 &= A_0e^{-kt} \\frac{0.5A_0}{A_0} &= e^{-kt} \\ln\left(\frac{0.5A_0}{A_0}\right) &= -kt \\ln(0.5) &= -kt \t &= \frac{\ln(0.5)}{-k}\end{align*}[/tex]

To find the time it would take to reduce the pollution level in each lake to 5% of its original level, we need to solve the equation[tex]$0.05A_0 = A_0e^{-kt}$[/tex] for t:

[tex]0.05A_0 &= A_0e^{-kt} \\frac{0.05A_0}{A_0} &= e^{-kt} \\ln\left(\frac{0.05A_0}{A_0}\right) &= -kt \\ln(0.05) &= -kt \t &= \frac{\ln(0.05)}{-k}\end{align*}[/tex]

The decay constant $k$ can be found by using the given information that each lake is replaced by clean water every 8 hours. This means that the half-life of the pollutant is 8 hours, which gives us:

[tex]0.5A_0 &= A_0e^{-k(8)} \\ln(0.5) &= -8k \k &= -\frac{\ln(0.5)}{8} \approx 0.08664\end{align*}[/tex]

Substituting this value of k into the equations we derived earlier, we get:

[tex]t_{50} = \frac{\ln(0.5)}{-k} \approx 8.006 \text{ hours}[/tex]

[tex]t_{5} &= \frac{\ln(0.05)}{-k} \approx 22.133 \text{ hours}[/tex]

Therefore, it would take approximately 8 hours to reduce the pollution level in each lake to 50% of its original level, and approximately 22 hours to reduce it to 5% of its original level.

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A) The length MN of the given wooden block is: 12

B) 80400 in²

1) The formula for volume of a cuboid is:

Volume = Length * Width * Height

Thus:

427 = (MN * 7 * 3) + (5 * 5 * 7)

427 = 21MN + 175

21MN = 252

MN = 252/21

MN = 12

2) Surface area of entire object is:

TSA = 2(12 * 3) + 2(12 * 7) - (5 * 7) + 2(7 * 3) + 3(5 * 7) + 2(5 * 5)

= 402 in²

For 200 blocks:

TSA = 200 * 402 = 80400 in²

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To achieve a margin of error of +3 for a 90% confidence interval for the average number of lawn mowings during summer in a large city with a mean of u and a standard deviation of 0-8.7, a simple random sample of size 18 should be selected.

To determine the sample size needed to achieve a margin of error of +3 for a 90% confidence interval, we can use the formula:

n = (z * σ / E)^2

where n is the sample size, z is the z-score for the desired confidence level (in this case, 1.645 for 90% confidence), σ is the standard deviation, and E is the margin of error.

Substituting the given values into the formula, we get:

n = (1.645 * 0.8 / 3)^2 = 17.18

Rounding up to the nearest whole number, we get a sample size of 18.

Therefore, to achieve a margin of error of +3 for a 90% confidence interval for the average number of lawn mowings during summer in a large city with a mean of u and a standard deviation of 0-8.7, a simple random sample of size 18 should be selected. This sample size ensures that the estimate of the population means based on the sample mean is within +3 of the true population mean with 90% confidence.

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The estimated sum of the given series using the sum of the first 10 terms is 302,500, the improved estimate for the sum of the given series is between 305,000 and 306,000, and the value of n is 8.

(a) Utilizing the equation for the entirety of the primary n terms of the arrangement, we have:

[tex]s10 = 1^3 + 2^3 + ... + 10^3[/tex]

= 1,000 + 8,000 + ... + 1,000,000

= 302,500

In this manner, the assessed whole of the given arrangement using the entirety of the primary 10 terms is 302,500.

(b) For n = 10, we have:

[tex]sn = 1^3 + 2^3 + ... + 10^3 ≈ 302,500[/tex]

Utilizing the disparities with[tex]f(x) = x^3[/tex], we have:

[tex]sn + ∫[10,∞] x^3 dx ≤ s ≤ sn + ∫[10,∞] x^3 dx + 10^3[/tex]

Utilizing calculus, ready to assess the integrand:

[tex]sn + ∫[10,∞] x^3 dx = sn + [1/4 x^4] [10,∞] = sn + 2500[/tex]

[tex]sn + ∫[10,∞] x^3 dx + 10^3 = sn + [1/4 x^4] [10,∞] + 10^3 = sn + 3500[/tex]

Substituting sn = 302,500, we get:

302,500 + 2500 ≤ s ≤ 302,500 + 3500

305,000 ≤ s ≤ 306,000

In this manner, the made strides assess for the sum of the given arrangement is between 305,000 and 306,000.

(c) The Leftover portion Gauge for the Necessarily Test states that the mistake E in approximating the whole s of an interminable arrangement by the nth halfway entirety sn is:

[tex]E ≤ ∫[n+1,∞] f(x) dx[/tex]

In this case, we need to discover mean of n such that E < 10 using the integral test

xss=removed xss=removed> [tex][(10^-5 x 4)^(1/4)] - 1[/tex]

n > 7.9378

Subsequently, we require n = 8 to guarantee that the blunder within the estimation s ≈ sn is less than[tex]10^-5.[/tex]

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The probability that the weight will be less than 4504 grams is 0.9713.

We need to standardize the value 4504 using the given mean and variance, and then use the standard normal distribution table to find the corresponding probability.

The standard deviation is the square root of the variance: √391876≈626.05

So, the z-score for a weight of 4504 grams is:

z=(4504−3315)/626.05≈1.8974

Using a standard normal distribution table, we find that the probability of a z-score being less than 1.8974 is approximately 0.9713.

Therefore, the probability that a newborn baby boy born at the local hospital will weigh less than 4504 grams is approximately 0.9713.

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Answer:

Point C is in the second quadrant. C is the correct answer.

Answer:

15 Pi m

Step-by-step explanation:

arc ADC = 360 Degrees - 60 Degrees divided by 360 Degrees Multiplied by 2 Pi Multiplied by 9

= 5/6 Times 18 Pi

 =15 Pi m

The test statistic for this sample is z = (0.254 - 0.203) / (p_hat * (1 - p_hat) * (1/n1 + 1/n2))

Based on the given information, we can set up the hypotheses as follows:

Null hypothesis: p1 - p2 = 0
Alternative hypothesis: p1 - p2 > 0

where p1 represents the proportion of successes in the first population and p2 represents the proportion of successes in the second population.

Since the sample sizes are large (n1 and n2 are not given, but we can assume they are large enough for the normal approximation to hold), we can use the normal distribution to approximate the sampling distribution of the difference in sample proportions.

The test statistic for this sample can be calculated as follows:

z = (p1 - p2) / sqrt(p_hat * (1 - p_hat) * (1/n1 + 1/n2))

where p_hat = (x1 + x2) / (n1 + n2), x1 and x2 are the number of successes in the two samples respectively.

Plugging in the given values, we get:

p_hat = (0.254n1 + 0.203n2) / (n1 + n2)
z = (0.254 - 0.203) / sqrt(p_hat * (1 - p_hat) * (1/n1 + 1/n2))

Since the significance level is not given, we cannot determine the critical value for the test. However, we can use the test statistic to calculate the p-value for the test, which is the probability of observing a difference in sample proportions as extreme as the one we observed (or more extreme) under the null hypothesis.

Once we have the p-value, we can compare it to the significance level to make a decision about whether to reject or fail to reject the null hypothesis.

Note: It is important to mention that using the normal approximation without the continuity correction may not always be accurate, especially when the sample sizes are small or the proportion of successes is close to 0 or 1. In such cases, it is recommended to use other methods (such as exact tests or simulation) that do not rely on the normal approximation.

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The number of hectares that the plane search is 129450 hectares

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

A search plane covers 50 square miles of countryside

This means that

Area = 50 square miles of countryside

As a general rule

1 square miles = 258.999 hectares

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

50 * 1 square miles = 258.999 hectares * 50

Evaluate

50 square miles = 129450 hectares

Hence, the number of hectares is 129450 hectares

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The probability that there really is a wolf when the boy cries wolf is 0.54.

Let A be the event that the boy cries wolf and B be the event that there is a wolf. We are given the following probabilities:

P(A|B) = 0.8 (the probability that the boy cries wolf when there is a wolf)

P(A|B') = 0.4 (the probability that the boy cries wolf when there is no wolf)

P(B) = 0.25 (the probability that there is a wolf)

We want to find P(B|A), the probability that there really is a wolf given that the boy cries wolf.

We can use Bayes' theorem to calculate this:

P(B|A) = P(A|B) * P(B) / [P(A|B) * P(B) + P(A|B') * P(B')]

Substituting the given values, we get:

P(B|A) = 0.8 * 0.25 / [0.8 * 0.25 + 0.4 * 0.75] = 0.54

Therefore, the probability that there really is a wolf when the boy cries wolf is 0.54.

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The ONB for the given plane in R3 is ū = [-5/√(26), 1/√(26), 0] and ī = [25/(√(26/13)), -5/(√(26/13)), 0].

To find an orthonormal basis for the plane x + 5y + 4z = 0, we first solve the system and get the parametric solution

x = -5t - 4s

y = t

z = s

Assigning parameters s and t to the free variables and writing the solution in vector form as su + tv, we get

[-5t - 4s, t, s] = t[-5, 1, 0] + s[-4, 0, 1]

Taking u = [-5, 1, 0] and v = [-4, 0, 1], we normalize u to have norm 1 by dividing it by its length

||u|| = √(26)

ū = [-5/√(26), 1/√(26), 0]

To find the component of v orthogonal to u, we take the dot product of v and u, and divide it by the dot product of u and u, and then multiply u by this scalar

v - ((v · u) / (u · u))u

v · u = -5

u · u = 26

v - (-5/26)[-5, 1, 0]

v - [25/26, -5/26, 0]

Finally, we normalize this vector to have norm 1

||v - proj_u v|| = √(26/13)

ī = [25/(2√(26/13)), -5/(2√(26/13)), 0]

Therefore, the orthonormal basis for the plane x + 5y + 4z = 0 is ū = [-5/√(26), 1/√(26), 0] and ī = [25/(√(26/13)), -5/(√(26/13)), 0].

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We apply the Gram-Schmidt process to these vectors to find an orthonormal basis:

v1 = x1 = [3, -4, 1

To find a basis of the null space N(A), we need to find all vectors x such that Ax = 0, where 0 is the zero vector.

To do this, we set up the augmented matrix [A | 0] and row reduce:

[ 1 2 1 3 2 | 0 ]

[ 4 1 0 6 1 | 0 ]

[ 1 1 2 4 5 | 0 ]

R2 - 4R1 -> R2:

[ 1 2 1 3 2 | 0 ]

[ 0 -7 -4 6 -7 | 0 ]

[ 1 1 2 4 5 | 0 ]

R3 - R1 -> R3:

[ 1 2 1 3 2 | 0 ]

[ 0 -7 -4 6 -7 | 0 ]

[ 0 -1 1 1 3 | 0 ]

R2 / -7 -> R2:

[ 1 2 1 3 2 | 0 ]

[ 0 1 4/7 -6/7 1 | 0 ]

[ 0 -1 1 1 3 | 0 ]

R1 - 2R2 - R3 -> R1:

[ 0 0 0 0 0 | 0 ]

[ 0 1 4/7 -6/7 1 | 0 ]

[ 0 0 11/7 -1/7 1 | 0 ]

We can write the system of equations corresponding to this row echelon form as:

x2 + (4/7)x3 - (6/7)x4 + x5 = 0

(11/7)x3 - (1/7)x4 + x5 = 0

Solving for the variables in terms of the free variables x3, x4, and x5, we get:

x1 = -[(4/7)x3 - (6/7)x4 - x5]/2

x2 = -(4/7)x3 + (6/7)x4 - x5

x3 = x3 (free variable)

x4 = x4 (free variable)

x5 = x5 (free variable)

So the null space N(A) is the set of all vectors of the form:

x = [ -[(4/7)x3 - (6/7)x4 - x5]/2, -(4/7)x3 + (6/7)x4 - x5, x3, x4, x5 ]

To find an orthogonal basis for N(A), we can use the Gram-Schmidt process. Let's call the columns of A a1, a2, a3, a4, and a5.

First, we need to find a basis for N(A) by setting the free variables to 1 and the others to 0:

x1 = [3, -4, 1, 0, 0]

x2 = [-2, 3, 0, 1, 0]

x3 = [-2, 1, 0, 0, 1]

Next, we apply the Gram-Schmidt process to these vectors to find an orthonormal basis:

v1 = x1 = [3, -4, 1

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The end behavior of the rational function is described as follows:

As x -> ∞, f(x) -> 1.

The horizontal asymptote is the value of f(x) as x goes to infinity, as long as this value is different of infinity.

For this problem, we have that both when x goes to negative infinity and when x goes to positive infinity, the graph of the function goes to y = 1, hence the end behavior of the function is defined by the horizontal asymptote as follows:

As x -> ∞, f(x) -> 1.

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The probability that exactly 12 of the selected people belong to the labor union is approximately 0.167 or 16.7%.

To solve this problem, we can use the binomial probability formula:
[tex]P(X = k) = (n choose k)p^{k}(1 - p)^{(n - k)}[/tex]

where:
- P(X = k) is the probability of getting k successes (12 in this case)
- n is the number of trials (20 in this case)
- p is the probability of success (belonging to the labor union, which is 3600/8000 or 0.45)
- (n choose k) is the number of ways to choose k items from a set of n items, which is given by the binomial coefficient formula (n! / (k! (n-k)!))

So, plugging in the values we get:

[tex]P(X = 12) = (20 choose 12) (0.45)^{12} (1 - 0.45)^{(20 - 12)}\\ = (167,960)(0.45)^{12}(0.55)^{8}\\ = 0.167[/tex]
Therefore, the probability that exactly 12 of the selected people belong to a labor union is approximately 0.167 or 16.7%.

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Answer:turn right 45 degrees, then turn right another 45 degrees. flip the figure x-axis wise/horizontally

Step-by-step explanation:

Answer:

To find the distance between two points, we can use the distance formula:

d = sqrt((x2 - x1)^2 + (y2 - y1)^2)

where (x1, y1) and (x2, y2) are the coordinates of the two points.

In this case, the two points are (3, 4) and (6, 8), so we have:

d = sqrt((6 - 3)^2 + (8 - 4)^2)

d = sqrt(3^2 + 4^2)

d = sqrt(9 + 16)

d = sqrt(25)

d = 5

Therefore, the distance between the points (3, 4) and (6, 8) is 5 units.

It's worth noting that the values 5√22 and √7 do not match the above

The correct answer is option (c) same engine rotational speed.

In a four-stroke engine, the camshaft rotates at half the speed of the crankshaft. The camshaft controls the opening and closing of the engine valves, which allows for the intake of fuel and air and the expulsion of exhaust gases.

When the engine rotational speed is doubled, the crankshaft will rotate twice as fast, but the camshaft will still rotate at half the speed of the crankshaft. This means that the camshaft will still rotate at the same speed as before, and the valve timing will remain the same.

Similarly, when the engine rotational speed is halved, the camshaft will still rotate at half the speed of the crankshaft. This means that the camshaft will still rotate at the same speed as before, and the valve timing will remain the same.

Therefore, the correct answer is option (c) same engine rotational speed.

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The variance of the given distribution is 0.9.

Calculating the anticipated value or distribution mean is the first step in determining a probability distribution's variance. The anticipated value is a weighted average of all potential outcomes, with each outcome's probability serving as the weight. The expected value can be expressed mathematically as follows:

[tex]E(X) =[/tex] Σ[tex][xi[/tex] × [tex]P(xi)][/tex]

where μ is the mean of the data.

Then, calculate μ:

μ [tex]= (1+2+3+4+5)/5[/tex]

[tex]=(15/5)[/tex]

[tex]= 3[/tex]

and replace this value and the values of xn and P(xn) into the formula for the variance, just as follow:

Hence, the variance of the given distribution is 0.9.

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f(x) is concave down on the interval -5 ≤ x ≤ -6 and 0 ≤ x ≤ 6.

f(x) is concave up on the interval -6 ≤ x ≤ 0.

To determine where f(x) is concave up or concave down, we need to calculate the second derivative of f(x):

f(x) = x √([tex]x^2[/tex] + 36)

f'(x) = √[tex]x^2[/tex] + 36) + [tex]x^2[/tex] √([tex]x^2[/tex] + 36)

f''(x) = (x ([tex]x^2[/tex] +72) )/(([tex]x^2[/tex]+36)[tex]^(3[/tex]/2))

To find where f(x) is concave up or concave down, we need to find where f''(x) > 0 (concave up) or f''(x) < 0 (concave down).

f''(x) = 0 when x = 0 or x = +/-6.

Thus, f(x) is concave down on the interval -5 ≤ x ≤ -6 and 0 ≤ x ≤ 6, and concave up on the interval -6 ≤ x ≤ 0.

The inflection point for this function is at x = 0.

To find the minimum and maximum for this function, we need to look at the endpoints and critical points of the interval -5 ≤ x ≤ 6.

f(-5) = -5√61 and f(6) = 6√72, so the minimum occurs at x = -5 and the maximum occurs at x = 6.

Therefore:

f(x) is concave down on the interval -5 ≤ x ≤ -6 and 0 ≤ x ≤ 6.

f(x) is concave up on the interval -6 ≤ x ≤ 0.

The inflection point for this function is at x = 0.

The minimum for this function occurs at x = -5.

The maximum for this function occurs at x = 6.

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The statements that is false concerning the hypothesis testing procedure for a regression model is "The null hypothesis is rejected if the adjusted r2 is above the critical value".

The statement that the null hypothesis is rejected if the adjusted r2 is above the critical value is false concerning the hypothesis testing procedure for a regression model.

The F-test statistic is used to test the overall significance of the regression model, and an α level must be selected to determine the level of significance.

The null hypothesis is that the true slope coefficient is equal to zero, which means that there is no linear relationship between the dependent variable and the independent variable.

The alternative hypothesis is that the true slope coefficient is not equal to zero, which means that there is a linear relationship between the dependent variable and the independent variable.

The adjusted R-squared value is a measure of the goodness of fit of the regression model and represents the proportion of the variation in the dependent variable that is explained by the independent variable(s) in the model.

The null hypothesis is rejected if the F-test statistic is above the critical value, which indicates that the regression model is statistically significant and the independent variable(s) have a significant linear relationship with the dependent variable.

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We can use the formula:

distance = rate × time

where distance is the total length of the road built, rate is the speed at which the road was built, and time is the number of weeks it took to build the road.

Substituting the given values, we get:

7.68 kilometers = 6 kilometers/week × time

To solve for time, we can divide both sides of the equation by 6 kilometers/week:

7.68 kilometers ÷ 6 kilometers/week = time

Simplifying the left-hand side, we get:

1.28 weeks = time

Therefore, it took the construction crew approximately 1.28 weeks to build the road.

The value of domain and range for this situation are,

Domain = (- ∞, ∞)

Range = (- ∞, ∞)

We have to given that;

The total cost for funding a trip for the senior class to go to the fall fair, C(x), is a function of the number of students that will make the trip, x.

Now, We have;

⇒ C (x) = 350 + 7.5x

Clearly, the function is a polynomial.

Hence, The value of domain and range for this situation are,

Domain = (- ∞, ∞)

Range = (- ∞, ∞)

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Answer:

Vertical angles are congruent, so the two triangles are congruent by AAS (Angle-Angle-Side).

None of the theorems listed can be used to show congruence.

The housing supply function is given by h(I,R) = a log1 + bR + cR log1. The housing supply responds to changes in I with a rate of a, and to changes in R with a rate of b + c log1.

Based on the given equation, the housing supply (h) is determined by two variables: I and R. The equation shows that h is a function of R, with a log-linear relationship. The variable I only appears as a constant (a) in the equation, so changes in I do not directly affect the supply of houses.
On the other hand, changes in R (or OR, which is the same variable) do affect the supply of houses. Specifically, an increase in R leads to an increase in the supply of houses. The magnitude of this increase depends on the values of b and c in the equation.
To see this, we can take the partial derivative of h with respect to R:
dh/dR = b + cR/(ln(10))
This equation tells us how much the housing supply changes in response to a change in R. The derivative is positive (i.e. the supply increases) as long as c is positive. The larger c is, the greater the increase in supply for a given increase in R.
Therefore, the correct answer is:
b + cR/(ln(10))

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The minimum cost of production is $278.46.

The base width of the box be x, then the base length is 2x. Let the height of the box be h.

The volume of the box is given by:

V = base area × height

[tex]70 = x[/tex] × [tex]2x[/tex] × [tex]h[/tex]

[tex]h = 35/x^2[/tex]

The cost of producing the box is given by:

[tex]C = 2[/tex] ×[tex](cost of top/bottom) + 4[/tex] × [tex](cost of side)[/tex]

[tex]C = 2[/tex]× [tex]9[/tex]× [tex](2x[/tex] × [tex]x) + 4[/tex] × [tex]6[/tex] × [tex](2x + 2h)[/tex]

[tex]C = 36x^2 + 48xh[/tex]

Substituting the expression for h obtained above:

[tex]C = 36x^2+ 48x(35/x^2)[/tex]

[tex]C = 36x^2+ 1680/x[/tex]

To minimize C, we take the derivative with respect to x and set it to zero:

[tex]dC/dx = 72x - 1680/x^2 = 0[/tex]

[tex]72x = 1680/x^2[/tex]

[tex]x^3 = 1680/72[/tex]

[tex]= 23.33[/tex]

x = 2.82 m (rounded to two decimal places)

Substituting this value of x in the expression for h, we get:

[tex]h = 35/(2.82)^2[/tex]

[tex]= 4.34 m[/tex] (rounded to two decimal places)

Therefore, the dimensions of the box that minimize the cost of production are:

[tex]Base width = x = 2.82 m[/tex]

[tex]Base length = 2x = 5.64 m[/tex]

[tex]Height = h = 4.34 m[/tex]

To find the minimum cost, we substitute these values of x and h in the expression for C:

[tex]C = 36(2.82)^2 + 1680/(2.82)[/tex]

[tex]C = $278.46[/tex] (rounded to two decimal places)

Therefore, the minimum cost of production is $278.46.

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The value of angle TUW is 32⁰.

The value of angle UTV is 25⁰.

The value of angle TUW is calculated by applying the following formula.

angle TVW = angle TUW (vertical opposite angles are equal)

angle TVW = 32⁰

So, angle TUW = 32⁰

The value of angle UTV is calculated as;

angle UTV = VWU (vertical opposite angles are equal)

3x + 4 = 2x + 11

3x - 2x = 11 - 4

x = 7

angle UTV = 3x + 4

= 3(7) + 4

= 25⁰

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