unit 11 volume and surface area homework 3 back page

The cost of the carpeting Gabriela will use in her living room is  $3,305.47 and Area of living room is 346.41 sq ft

Area of rectangular room = length x width = 25 ft x 16 ft = 400 sq ft

Area of quarter-circle = (1/4) x pi x r^2 = (1/4) x pi x 8^2 = 16 pi sq ft

So the area of the living room is:

Area of living room = Area of rectangular room - Area of quarter-circle

= 400 sq ft - 16 pi sq ft

= 346.41 sq ft

The cost of carpeting this area is:

Cost of carpeting = Area of living room x Cost per square foot

= 346.41 sq ft x $9.55/sq ft

≈ $3,305.47

Therefore, the cost of the carpeting Gabriela will use in her living room is $3,305.47.

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The exponential regression equation for the set of data is given as follows: y = 931.61(1.1)^x.

The number of bacteria after 16 hours is given as follows:

4,281 bacteria.

An exponential function has the definition presented as follows:

y = ab^x.

In which the parameters are given as follows:

For exponential regression, we must insert the points of a data-set into an exponential regression calculator.

The points for this problem are given as follows:

(0, 940), (1, 1034), (2, 1105), (3, 1223), (4, 1352), (5, 1520).

Inserting these points into a calculator, the equation is given as follows:

y = 931.61(1.1)^x.

The number of bacteria after 16 hours is given as follows:

y = 931.61 x (1.1)^16

y = 4,281 bacteria.

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Jane's total combined monthly cost for auto loan payment and insurance is $736.67 (rounded to the nearest dollar).

If Jane's monthly car insurance payment of $170 is same to 30% of her monthly car loan fee, then we are able to set up the subsequent equation:

0.3x = 170

Where x is the monthly auto loan fee. To solve for x, we will divide each facets by using 0.3:

x = 170 / 0.3 = $566.67

So, Jane's monthly auto loan charge is $566.67.

To discover her general combined monthly price for auto loan price and insurance, we simply upload her monthly car coverage charge to her monthly auto loan payment:

$566.67 + $170 = $736.67

Consequently, Jane's total combined monthly cost for auto loan payment and coverage is $736.67

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In a perfectly competitive market, when the price is greater than the minimum average total cost for all firms, it indicates that the firms are experiencing economic profits. Here's a step-by-step explanation:

1. In a perfectly competitive market, there are numerous firms producing a homogeneous product with free entry and exit for new firms.

2. The demand curve for each firm is perfectly elastic (horizontal), which means that firms can sell any quantity of the product at the market price without affecting the price.

3. The average total cost (ATC) includes both the average variable cost (AVC) and the average fixed cost (AFC). The ATC curve is U-shaped, with the minimum ATC representing the lowest average cost per unit.

4. When the market price is greater than the minimum ATC, it means that the firms are covering all their variable and fixed costs, with additional revenue left over as profit.

5. In this scenario, the firms are earning economic profits, which can be calculated by subtracting the ATC from the market price and multiplying the result by the quantity produced.

6. Economic profits attract new firms to enter the market, leading to increased competition. This, in turn, drives down the market price, reducing economic profits.

7. Over time, as more firms enter and exit the market, the price will eventually reach a point where it is equal to the minimum ATC. This is the long-run equilibrium, where firms earn zero economic profits, covering all their costs but not earning any additional profit.

To summarize, in a perfectly competitive market, when the price is greater than the minimum average total cost for all firms, it indicates that the firms are experiencing economic profits. These profits will attract new firms to enter the market, which will eventually lead to the price falling to the minimum ATC in the long-run equilibrium.

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The p-value for a two-tailed test is 0.0769.

The null hypothesis (H0) is that the population mean weight of active ingredient per tablet is 5 grams. The alternative hypothesis (Ha) is that the population mean weight of active ingredient per tablet is not equal to 5 grams.

H0: µ = 5

Ha: µ ≠ 5

The significance level is 5%.

The test statistic is t = (x - µ) / (s / √n), where x is the sample mean, µ is the hypothesized population mean, s is the sample standard deviation, and n is the sample size.

The decision rules: Reject H0 if |t| > tα/2,n-1, where tα/2,n-1 is the t-value from the t-distribution with n-1 degrees of freedom and α/2 level of significance.

Calculating the test statistic and p-value:

x = (5.01 + 4.69 + 5.03 + 4.98 + 4.98 + 4.95 + 5.00 + 5.00 + 5.03 + 5.01 + 5.04 + 4.95) / 12 = 4.9983

s = sqrt([(5.01 - 4.9983)² + (4.69 - 4.9983)² + ... + (4.95 - 4.9983)²] / 11) = 0.0383

t = (4.9983 - 5) / (0.0383 / sqrt(12)) = -1.931

Degrees of freedom = n-1 = 11

At α = 0.05, t0.025,11 = 2.201

The p-value for a two-tailed test is P(|t| > 1.931) = 0.0769.

Interpretation: Since the p-value (0.0769) is greater than the significance level (0.05), we fail to reject the null hypothesis. There is not enough evidence to conclude that the population mean weight of active ingredient per tablet is different from 5 grams at the 5% level of significance.

Assumptions: We assume that the sample is randomly selected and comes from a normally distributed population. We also assume that the sample standard deviation is a good estimate of the population standard deviation.

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To test your hypothesis that the average time to deliver pizza, once the order is placed, is greater than 25 minutes in the population, you would perform a one-sample T-test.

1. Formulate the null hypothesis (H0) and the alternative hypothesis (H1).

In this case, H0: the average delivery time is equal to 25 minutes, and H1: the average delivery time is greater than 25 minutes.

2. Collect a sample of delivery times and calculate the sample mean and sample standard deviation.

3. Determine the appropriate T-distribution based on your sample size (degrees of freedom = sample size - 1).

4. Calculate the T-statistic using the sample mean, sample standard deviation, and sample size.

5. Determine the critical T-value based on your chosen level of significance (e.g., 0.05) and the one-tailed T-distribution.

6. Compare the calculated T-statistic to the critical T-value. If the T-statistic is greater than the critical T-value, you can reject the null hypothesis in favor of the alternative hypothesis.

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The value of x is 88.22 units.

Given is right triangle, we need to find the value of x,

tan 60° = 85 / a

a = 85 / √3

a = 49

Now,

tan 30° = 85 / a + x

a+x = 85 ÷ 1/√3

a+x = 147.22

x = 147.2-49

x = 88.22

Hence, the value of x is 88.22 units.

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To decrease the width of a confidence interval while maintaining the same level of confidence, you can increase your sample size i.e., Option C is the correct answer.

A confidence interval is a range of values that is likely to contain the true population parameter with a certain level of confidence. The width of the confidence interval is influenced by several factors, including the sample size, level of confidence, and population standard deviation.

When the sample size is increased, the standard error of the sample mean decreases, which in turn decreases the width of the confidence interval. This means that a larger sample size provides more precise estimates of the population parameter and reduces the variability of the sample means. As a result, a larger sample size allows for a narrower confidence interval while maintaining the same level of confidence.

On the other hand, decreasing the sample size can widen the confidence interval, as there are less data available to estimate the population parameter. Increasing the significance level or decreasing the population standard deviation may also widen the confidence interval, as this increases the range of plausible values for the population parameter.

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Answer: Side C = 17.5

Step-by-step explanation: We have to follow the laws of sines. So we would do 25sin(42)/sin(107).

Or sin(42) x 25

Then divide that value by sin(107).

By the Comparison Test, we can conclude that Σ (4sin(n))/([tex]n^2[/tex] - 1) converges.

We can use the Limit Comparison Test to determine the convergence or divergence of the series:

∑(4 sin(n))/n

To do this, we need to find a series whose convergence is known and which can be compared to the given series. Let's choose the series ∑(1/n) since we know that it diverges.

We take the limit of the ratio of the nth term of each series as n approaches infinity:

lim n→∞ (4 sin(n)/n)/(1/n) = lim n→∞ 4 sin(n) = DNE

Since the limit does not exist, we cannot apply the Limit Comparison Test. Therefore, Therefore, by the Comparison Test, we can conclude that Σ (4sin(n))/([tex]n^2[/tex] - 1) converges.

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Option E is incorrect because we have enough evidence to reject the null hypothesis.

The correct answer is (C) There is evidence that μ ≠ 70.

The null hypothesis is that the true mean tire pressure for the population of tires on city buses in this city is equal to 70psi, i.e., H0: μ = 70. The alternative hypothesis is that it is not equal to 70psi, i.e., Ha: μ ≠ 70.

The p-value of 0.043 is less than the significance level of 0.05. Therefore, we reject the null hypothesis and conclude that there is evidence that the true mean tire pressure is not equal to 70psi.

Option A is incorrect because we cannot conclude that the true mean tire pressure is less than 70psi. Option B is incorrect because we cannot conclude that the true mean tire pressure is equal to 70psi. Option D is incorrect because the p-value is not the probability that μ = 70. Option E is incorrect because we have enough evidence to reject the null hypothesis.

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The formula for standard error that applies to this problem is SE = sqrt[p(1-p)/n], where SE represents the standard error, p represents the probability of success, and n represents the sample size. In this case, N represents the population size, but it is not necessary for calculating the standard error.

Substituting the values given in the problem, we have:

SE = sqrt[0.45(1-0.45)/44] = 0.0717 (rounded to four decimal places)

Therefore, the standard error for this problem is 0.0717. This value represents the degree of variability or uncertainty in the sample proportion, or the degree to which the sample proportion is likely to deviate from the true population proportion. A larger sample size or a more extreme probability of success (closer to 0 or 1) would result in a smaller standard error, indicating greater precision in the estimate of the population proportion.

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The points lie on a vertical line passing through the point x = -1.

We have,

The two points (-1, 6) and (-1, -6) have the same x-coordinate but different y-coordinates.

This means that they lie on a vertical line passing through the point x = -1.

When graphed on a coordinate plane, the line would appear as a vertical line at x = -1, with one point above the x-axis and the other point below the x-axis.

Thus,

The points lie on a vertical line passing through the point x = -1.

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The 95% confidence interval for the population mean is (54.70, 70.96).

To find the 95% confidence interval for the population mean, we need to first find the sample mean and sample standard deviation.

Sample Mean (x) = (60.3 + 65.2 + 60 + 70.6 + 62 + 55.9) / 6 = 62.33

Sample Standard Deviation (s) = sqrt([(60.3 - 62.33)^2 + (65.2 - 62.33)^2 + (60 - 62.33)^2 + (70.6 - 62.33)^2 + (62 - 62.33)^2 + (55.9 - 62.33)^2] / (6 - 1)) = 5.28

Next, we can use the formula for the confidence interval:

CI = x ± t*(s/sqrt(n))

where x is the sample mean, s is the sample standard deviation, n is the sample size, and t is the t-value for the 95% confidence level with (n-1) degrees of freedom.

From a t-distribution table with 5 degrees of freedom (n-1 = 6-1 = 5) and a 95% confidence level, we find that the t-value is 2.571.

Plugging in the values, we get:

CI = 62.33 ± 2.571*(5.28/sqrt(6))

CI = (54.70, 70.96)

Therefore, the 95% confidence interval for the population mean is (54.70, 70.96).

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Based on the provided information, f'(x) changes from positive to negative at x=1, indicating that the function has a local maximum at this point.

Since y=f(x) is continuous for all real numbers and the interval is [0, ∞), there is an absolute maximum at x=1. The best description of the absolute extrema is: "There is an absolute maximum at x=1." Based on the sign chart for the first derivative, we know that the function is increasing from negative infinity to x=-1, and then decreasing from x=-1 to positive infinity. This means that there is an absolute maximum at x=-1 since the function is increasing to that point and decreasing after it. Therefore, the correct statement is: "There is an absolute maximum at x=-1."

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To approximate f'(0.6) using the Second Derivative Midpoint Formula with the given table data points and h = 0.06, follow these steps:

1. Identify the relevant data points: f(0.54), f(0.6), and f(0.66).
2. Apply the Second Derivative Midpoint Formula: f'(0.6) ≈ (f(0.66) - 2f(0.6) + f(0.54)) / (h^2).

Unfortunately, I cannot provide a specific answer without the values for f(0.54), f(0.6), and f(0.66).

Please provide these values, and I will gladly help you complete the calculation.

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The probability that a customer will have to wait more than 2 minutes is 0.0099.

(a) Since the waiting time at the supermarket is assumed to be exponentially distributed, the probability density function is given by:

f(x) = (1/B)e^(-(x/B)) for x ≥ 0

= 0 elsewhere

where B is the mean waiting time. In this case, the mean waiting time is 19 seconds. Therefore, the probability density function of waiting time at the supermarket is:

f(x) = (1/19)e^(-(x/19)) for x ≥ 0

= 0 elsewhere

(b) To find the probability that a customer will have to wait between 15 and 30 seconds, we need to find the area under the probability density function between x=15 and x=30. This can be calculated using the cumulative distribution function (CDF) of the exponential distribution:

P(15 ≤ x ≤ 30) = ∫15^30 f(x)dx = ∫15^30 (1/19)e^(-(x/19)) dx

Using integration by substitution, let u = -(x/19), then du/dx = -1/19 and dx = -19 du:

P(15 ≤ x ≤ 30) = ∫-(15/19)^-(30/19) e^udu = e^(-(15/19)) - e^(-(30/19))

P(15 ≤ x ≤ 30) ≈ 0.2462 (rounded to four decimal places).

Therefore, the probability that a customer will have to wait between 15 and 30 seconds is 0.2462.

(c) To find the probability that a customer will have to wait more than 2 minutes, we need to find the area under the probability density function for x > 120 seconds (2 minutes). This can be calculated using the CDF of the exponential distribution:

P(x > 120) = ∫120^∞ f(x)dx = ∫120^∞ (1/19)e^(-(x/19)) dx

Using integration by substitution, let u = -(x/19), then du/dx = -1/19 and dx = -19 du:

P(x > 120) = ∫-(120/19)^-∞ e^udu = e^(-(120/19))

P(x > 120) ≈ 0.0099 (rounded to four decimal places).

Therefore, the probability that a customer will have to wait more than 2 minutes is 0.0099.

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The probability of getting heads or tails when flipping a coin is 0.5 or 50%. The final probability will give you the chance of getting between 4900 and 5100 heads when flipping a fair coin 10,000 times.

However, the probability of getting a certain number of heads when flipping a coin, a certain number of times can be calculated using probability theory. In this case, the probability of getting between 4900 and 5100 heads when flipping a fair coin 10000 times can be calculated using the binomial distribution formula.

The binomial distribution formula is P(X=k) = (n choose k) * p^k * (1-p)^(n-k), where P(X=k) is the probability of getting k heads, n is the number of coin flips, p is the probability of getting a head (0.5 in this case), and (n choose k) is the binomial coefficient.

Using this formula, the probability of getting between 4900 and 5100 heads when flipping a fair coin 10000 times is approximately 0.023 or 2.3%. This means that out of 1000 trials, you can expect to get between 4900 and 5100 heads around 23 times.

In summary, the probability of getting between 4900 and 5100 heads when flipping a fair coin 10000 times is around 2.3%. This probability can be calculated using the binomial distribution formula, which takes into account the number of coin flips and the probability of getting a head.

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The solution is, the solutions using the Zero Product Property: is x =0 and 3/4.

The expression to be solved is:

4x² - 3x = 0

we know that,

The zero product property states that the solution to this equation is the values of each term equals to 0.

now, we have,

4x² - 3x = 0

or, x ( 4x - 3 ) = 0

i.e. we get,

x × ( 4x - 3 ) = 0

so, using the Zero Product Property:

we get,

x = 0

or,

( 4x - 3 ) = 0

so, we have,

x = 0 or, x = 3/4

The answers are 0 and 3/4.

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The total number of fourth-grade students in the school is 144

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

This means that

Fourth-grade students = 6 * Ms. Smyth's fourth-grade class.

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

Fourth-grade students = 6 * 24

Evaluate

Fourth-grade students = 144

Hence, the total number of students is 144

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The volume of the whole lime represented by a sphere is given by  12 cubic centimeters.

Volume of the lime in hemispherical shape is equal to

= 6 cubic centimeters

Let us consider 'r' be the radius of the sphere.

Volume of hemisphere = ( 2/3 ) πr³

Volume of a sphere = ( 4 /3) πr³

Relation between volume of hemisphere and volume of a whole lime sphere

Volume of a whole lime sphere = 2 times of volume of hemisphere

⇒Volume of a whole lime sphere =  2 × 6

⇒Volume of a whole lime sphere =  12 cubic centimeters.

Therefore, the volume of the whole lime is equal to  12 cubic centimeters.

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If p represents the current population, the expression that represents the expected population next year is C. [tex]1.075p[/tex].

To find the expected population next year, we need to add the current population to the percentage increase.

The percentage increase is 7.5% of the current population which can be expressed as 0.075p. So, expression that represents the expected population of the city next year will be:

= Current population + Percentage increase

= p + 0.075p

= 1.075p.

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Answer:

14

Step-by-step explanation:

eere4

If a given certificate C is a legitimate answer to the instance, we can check in polynomial time if it is (B, S1, S2, ..., Sm, K) of SETCOVER. Hence, the language SETCOVER is in NP.

To prove that the language SETCOVER is in NP, we need to show that given an instance (B, S1, S2, ..., Sm, K) of SETCOVER and a certificate C, we can verify in polynomial time whether C is a valid solution to the instance.

The certificate C in this case is a subset IC of {1, 2, ..., m} of size K, which represents the indices of the sets that form a cover for B. To verify whether C is a valid solution, we need to check two things:

Verify that IC has size K: We can simply count the number of elements in IC and check if it equals K. This can be done in O(m) time, which is polynomial in the size of the input.

Verify that the sets S_i for i in IC form a cover for B: We can iterate through the elements in B and check whether each element is present in at least one of the sets S_i, where i is in IC. Since B has at most |B| elements, and each set S_i has at most |B| elements, this can be done in O(K |B|) time, which is polynomial in the size of the input.

Therefore, If a given certificate C is a legitimate answer to the instance, we can check in polynomial time if it is (B, S1, S2, ..., Sm, K) of SETCOVER. Hence, the language SETCOVER is in NP.

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If Patricia needs to have $30,000 for her daughter's college tuition that is due in exactly 2 years, she should invest C. $26, 654.70 (present value) in an account paying 6% interest compounded semi-annually.

The present value describes the current investment needed to earn a future value.

The present value can be determined using the PV formula or an online finance calculator.

N (# of periods) = 4 semi-annual periods (2 years x 2)

I/Y (Interest per year) = 6%

PMT (Periodic Payment) = $0

FV (Future Value) = $30,000

Results:

Present Value (PV) = $26,654.70

Total Interest = $3,345.30

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Answer:

A, C, E

Step-by-step explanation:

remember to law of sine :

a/sin(A) = b/sin(B) = c/sin(C)

or the "upside-down" version :

sin(A)/a = sin(B)/b = sin(C)/c

with a, b, c being the sides of the triangle, and A, B, C being the corresponding opposite angles in the triangle.

so, as you can clearly see, we always need at least one angle and one side (in fact either 2 angles one side or 1 angle 2 sides) to use the law of sine to solve the rest of the triangle.

therefore, the answer options A, C, E are correct.

for SSS (all 3 sides are known) we need the law of cosine to solve the angles (at least one of them, and then we could continue with either law).

remember :

c² = a² + b² - 2ab×cos(C)

again, a,b,c are the sides, and C is the opposite angle of whatever side we define as "c".

that's why I always call this the extended Pythagoras.

for AAA (all 3 angles are known) we cannot solve the triangle, because dilated triangles all have the same angles. and therefore there are infinitely many triangles with the same angles.

Answer:

1, 768

2, 2088

3, 7789

4, 1828

5, 1435.

To find the surface area of the given surface using cylindrical coordinates, first we need to find the parametrization of the surface. Since you have not provided the explicit form of the surface, I'll provide you with a general procedure.

Let's consider a surface S given by the equation G(r, θ, z) = 0, where r and θ are cylindrical coordinates.

1. Parametrize the surface:
To parametrize the surface, express it in terms of two parameters (say, r and θ). Then, a parametrization of the surface can be given as:

R(r, θ) = (r*cos(θ), r*sin(θ), z(r, θ))

2. Compute the partial derivatives:
Now, compute the partial derivatives of R with respect to r and θ:

R_r = (∂R/∂r) = (cos(θ), sin(θ), ∂z/∂r)
R_θ = (∂R/∂θ) = (-r*sin(θ), r*cos(θ), ∂z/∂θ)

3. Cross product and magnitude:
Calculate the cross product of these partial derivatives and find its magnitude:

N = R_r × R_θ = (a, b, c)
|M| = sqrt(a^2 + b^2 + c^2)

4. Set up the double integral:
Finally, set up the double integral to find the surface area of S:

Surface Area = ∬_D |M| dr dθ

Here, D is the domain of the parameters r and θ on the surface. To evaluate the integral, you will need to know the specific form of the surface and the limits of integration for r and θ.

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At a significance level of 0.01, the conclusion would be that there is not enough evidence to support the alternative hypothesis (H1: μ > 72).

To lea

a. To calculate the p-value, we can use the standard normal distribution and the test statistic formula:

Test statistic (Z) = (sample mean - population mean) / (population standard deviation / sqrt(sample size))

Z = (75.2 - 72) / (6 / sqrt(16))

Z = 3.2 / 1.5

Z = 2.13 (rounded to two decimal places)

To find the p-value, we need to calculate the area under the standard normal curve to the right of the test statistic (Z = 2.13). Using a standard normal distribution table or a calculator, we find that the area to the right of Z = 2.13 is approximately 0.016.

Since this is a one-sided test (H1: μ > 72), the p-value is the probability of observing a test statistic as extreme or more extreme than the one obtained. Therefore, the p-value is 0.016.

b. If the test were made at a significance level of 0.01 (1%), we would compare the p-value to the significance level. In this case, the p-value (0.016) is less than the significance level (0.01). Therefore, we would reject the null hypothesis.

c. To find the critical value at a significance level of 0.01, we need to determine the z-score that corresponds to an area of 0.01 in the upper tail of the standard normal distribution.

Using a standard normal distribution table or a calculator, we find that the critical value for a significance level of 0.01 is approximately 2.33.

Comparing the critical value (2.33) with the test statistic (Z = 2.13), we see that the test statistic is less than the critical value. In hypothesis testing, if the test statistic is less than the critical value, we fail to reject the null hypothesis.

Therefore, at a significance level of 0.01, the conclusion would be that there is not enough evidence to support the alternative hypothesis (H1: μ > 72).

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Isaac pay for the cost of racket is 39.9.

The cost of a pair of shoes is three times the cost of a bag, so we can write:

Cost of shoes = 3b

Similarly, the cost of the racket is twice the cost of the bag, so we can write:

Cost of racket = 2b

Now we can use the given information to set up an equation:

Cost of racket + Cost of bag + Cost of shoes = $119.70

Substituting the expressions we found above, we get:

2b + b + 3b = $119.70

Simplifying the equation:

6b = $119.70

Dividing both sides by 6:

b = $19.95

So the cost of the bag is $19.95.

We can use this to find the costs of the shoes and racket:

Cost of shoes = 3b = 3($19.95) = $59.85

Cost of racket = 2b = 2($19.95) = $39.90

Therefore, Isaac paid $39.90 for the racket.

A cost is an expenditure required to produce or sell a product or get an asset ready for normal use. In other words, it's the amount paid to manufacture a product, purchase inventory, sell merchandise, or get equipment ready to use in a business process.

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The decision with the highest expected value should be chosen. In this case, the economics book has a higher expected value (32,000 copies) compared to the statistics book (30,500 copies). Therefore, the optimum decision alternative is to write the economics book.

a. Decision tree diagram:
2. Branch off two nodes from the root for each option.
3. From the economics book node, branch off two nodes for major and smaller publisher placement. Assign probabilities of 50% and 50% for each.
4. From the statistics book node, branch off two nodes for major and smaller publisher placement. Assign probabilities of 40% and 60% for each.
5. Assign ultimate sales to each end node (40,000 and 30,000 for economics; 50,000 and 35,000 for statistics).

b. The probability that the economics book would wind up being placed with a smaller publisher is 50% (1 - 50% chance of placing it with a major publisher).

c. The probability that the statistics book would wind up being placed with a smaller publisher is 60% (1 - 40% chance of placing it with a major publisher).

d. Expected value for the decision alternative to write the economics book:
(0.50 * 40,000) + (0.50 * 0.80 * 30,000) = 20,000 + 12,000 = 32,000 copies.

e. Expected value for the decision alternative to write the statistics book:
(0.40 * 50,000) + (0.60 * 0.50 * 35,000) = 20,000 + 10,500 = 30,500 copies.

f. Expected value for the optimum decision alternative:
The decision with the highest expected value should be chosen. In this case, the economics book has a higher expected value (32,000 copies) compared to the statistics book (30,500 copies). Therefore, the optimum decision alternative is to write the economics book.

For more questions on optimum decisions - https://brainly.com/question/25870371

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