P(A)=[tex]\frac{9}{20}[/tex],P(B)=[tex]\frac{3}{5}[/tex],P(A∩B)=[tex]\frac{27}{100}[/tex] ,P(A∪B)=?

The value of y' is found to be approximately 0.748 ft.

The specific energy, E, in open channel flow, can be calculated using the equation E = [tex]y + Q^2/(2gy^2)[/tex] where y is the depth of flow, Q is the flow rate, and g is the acceleration due to gravity.

In this case, Q = y * width * velocity = [tex]2 ft * 5 ft * 7.5 ft/s = 75 ft^3/s.[/tex]

Substituting these values in, the specific energy, E, is found to be E = 2 ft + (75 ft³/s)² / (2 * 32.2 ft/s² * (2 ft)²) = 3.466 ft.

The alternate depth, y', can be found by solving the equation 3.466 ft

= [tex]y' + (75 ft^3/s)^2 / (2 * 32.2 ft/s^2 * (y')^2) for y'.[/tex]

This is a quadratic equation and using the positive root for supercritical flow, y' is found to be approximately 0.748 ft.

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The correct option is the third one, the explicit formula for the sequence is:

aₙ = 52 - 5n

Here we have what it seems to be an arithmetic sequence:

47, 42, 37, 32, ...

We can see that the difference between each pair of consecutive terms is -5, so that is the common difference of the sequence:

42 - 47 = -5

37 - 42 = -5

32 - 37 = -5

Then the explicit formula for the arithmetic sequence is:

aₙ = a₁ + (n - 1)*d

Where a₁ is the initial value, and d is the common difference.

So the explicit formula is:

aₙ = 47 + (n - 1)*(-5)

We can simplify this to get:

aₙ = 47  - 5n + 5

aₙ = 52 - 5n

The correct option is the third one.

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The area of the figure is 84 in².

We have,

The figure has two shapes.

Trapezium and a triangle.

Now,

Area of the trapezium.

= 1/2 x (14 + 25) x (2 + 2)

= 1/2 x 39 x 4

= 78 in²

And,

Area of the triangle.

= 1/2 x 4 x 3

= 1/2 x 4 x 3

= 6 in²

Now,

Area of the figure.

= 78 + 6

= 84 in²

Thus,

The area of the figure is 84 in².

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Answer: Your answer is D. 3.5

Step-by-step explanation: 28 divided by 8 is 8.5. I learned how to divide numbers on paper in last years math class.

Hope it helped :D

Answer:

D. 3.5

Step-by-step explanation:

We just take 28 divided by 8 and get 3.5

The equilibrium price and quantity of gasoline are $3.33 per gallon and 833.33 gallons respectively.

To find the equilibrium price and quantity, we need to set the demand equal to the supply:

5 - 0.002q = 0.2 + 0.004q

Solving for q, we get q = 833.33 gallons.

To find the equilibrium price, we can substitute q back into either the demand or supply equation. Using the demand equation, we get p = $3.33 per gallon.

Therefore, the equilibrium price and quantity of gasoline are $3.33 per gallon and 833.33 gallons respectively.

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The solution to x' = jx using exp(jt) is x(t) = ce^(jt), where c is a constant.

We start by assuming that x(t) = ce^(jt), then taking its derivative we get x'(t) = c(j)e^(jt). We substitute these values into the equation x' = jx and get c(j)e^(jt) = jce^(jt). We can then divide both sides by ce^(jt) to get j = j, which is true. This means that our assumption of x(t) = ce^(jt) is valid, and the solution is x(t) = ce^(jt).

The exponential function e^(jt) is a complex-valued function that can be used to represent sinusoidal functions with angular frequency t. In this case, we use it to represent the solution to the differential equation x' = jx. By assuming that x(t) is of the form ce^(jt), we are essentially saying that the function x(t) is a sinusoidal function with angular frequency t, and that its amplitude is a constant c.

The solution to x' = jx using exp(jt) is x(t) = ce^(jt), where c is a constant. This solution represents a sinusoidal function with angular frequency t, and its amplitude is a constant c.

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The dimensions that require the minimum amount of material for the open-top box are:

Length = 8 inches, Width = 8 inches, Height = 4 inches.

To find the dimensions that minimize the amount of material needed, we can approach the problem by using calculus and optimization techniques. Let's denote the length of the square bottom as "x" inches and the height of the box as "h" inches. Since the volume of the box is given as 256 cubic inches, we have the equation:

Volume = Length × Width × Height = x² × h = 256.

To minimize the material used, we need to minimize the surface area of the box. The surface area consists of the bottom area (x²) and the combined areas of the four sides (4xh). Therefore, the total surface area (A) is given by the equation:

A = x² + 4xh.

We can solve for h in terms of x using the volume equation:

h = 256 / (x²).

Substituting this expression for h in terms of x into the surface area equation, we get:

A = x² + 4x(256 / (x²)).

Simplifying further, we obtain:

A = x² + 1024 / x.

To minimize A, we take the derivative of A with respect to x, set it equal to zero, and solve for x:

dA/dx = 2x - 1024 / x² = 0.

Solving this equation yields x = 8 inches. Plugging this value back into the equation for h, we find h = 4 inches.

Therefore, the dimensions that require the minimum amount of material are: Length = 8 inches, Width = 8 inches, and Height = 4 inches.

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The value for the new mean, after removing a score with a value of X = 4 from the population, is c. 66/7.

To calculate the new mean, we need to subtract the score that is removed from the original sum of scores and then divide by the new number of scores.

Given that the population originally has N = 7 scores with a mean of μ = 10, the sum of the scores is N * μ = 7 * 10 = 70.

When the score of 4 is removed, the sum of the remaining scores becomes 70 - 4 = 66. The new number of scores is N - 1 = 7 - 1 = 6.

Therefore, the new mean is 66/6 = 11.

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As the sample size (n) increases, the power of the statistical test also increases.

The power of a statistical test measures the ability of the test to detect a true effect or reject a false null hypothesis. In this case, the investigator states that the power of his test at a significance level of 1% is 0.87. If the sample size (n) increases, the power of the test increases.

Increasing the sample size generally leads to an increase in the power of a statistical test. This is because a larger sample size provides more information and reduces the variability in the data. With a larger sample size, the test has a greater chance of detecting a true effect and rejecting the null hypothesis when it is false. Consequently, the power of the test increases.

In summary, as the sample size (n) increases, the power of the statistical test also increases. This is because a larger sample size enhances the test's ability to detect true effects and reject false null hypotheses, resulting in higher statistical power. Therefore, in this scenario, increasing the sample size would lead to an increase in the power of the test.

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The correct choice is A. Using the cofactor expansion across the first row, the determinant of the given matrix is (2)(-13) - (-3)(-5) + (3)(7) = -26 + 15 + 21 = 10.

To compute the determinant using the cofactor expansion across the first row, we multiply each element of the first row by its cofactor and sum them up. The cofactor of an element is determined by taking the determinant of the submatrix obtained by removing the row and column containing that element, and then multiplying it by (-1) raised to the power of the sum of the row and column indices.

For the given matrix:

2  2  1

-3 1  4

3  3 -1

Expanding along the first row, we have:

Det = (2)(cofactor of 2) + (2)(cofactor of 2) + (1)(cofactor of 1)

    = (2)(-13) - (-3)(-5) + (3)(7)

    = -26 + 15 + 21

    = 10.

Therefore, the correct choice is A. The determinant of the matrix, using the cofactor expansion across the first row, is 10.

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The population will reach 10,000 after about 166.68 minutes.

We can use the formula for exponential growth: N = N0 * e^(rt), where N is the population at time t, N0 is the initial population, r is the growth rate, and e is Euler's number.

Let's use the first two data points to find the growth rate and initial population. We know that after 15 minutes, N = 400, so:

400 = N0 * e^(r*15)

Similarly, after 30 minutes, N = 1400, so:

1400 = N0 * e^(r*30)

Dividing the second equation by the first, we get:

3.5 = e^(r*15)

Taking the natural logarithm of both sides, we get:

ln(3.5) = r*15

So the growth rate is:

r = ln(3.5)/15

r ≈ 0.0918

Using the first equation above, we can solve for N0:

400 = N0 * e^(0.0918*15)

N0 ≈ 98.51

So the initial population was about 98.51.

The doubling period is the time it takes for the population to double in size. We can use the formula for doubling time: T = ln(2)/r, where T is the doubling time.

T = ln(2)/0.0918

T ≈ 7.56 minutes

So the doubling period is about 7.56 minutes.

To find the population after 120 minutes, we plug in t = 120:

N = 98.51 * e^(0.0918*120)

N ≈ 22601.27

So the population after 120 minutes is about 22,601.27.

To find when the population reaches 10,000, we set N = 10,000 and solve for t:

10,000 = 98.51 * e^(0.0918*t)

t = ln(10,000/98.51)/0.0918

t ≈ 166.68 minutes

So the population will reach 10,000 after about 166.68 minutes.

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To prove that if p is an odd prime and p = a^2 * b^2 for integers a, b, then p ≡ 1 (mod 4), we can use the concept of quadratic residues and the properties of modular arithmetic.

Let's start with the given assumption that p is an odd prime and can be expressed as p = a^2 * b^2, where a and b are integers. We want to prove that p ≡ 1 (mod 4), which means p leaves a remainder of 1 when divided by 4.

We can begin by considering the possible residues of perfect squares modulo 4. When a is an even integer, a^2 ≡ 0 (mod 4) since the square of an even number is divisible by 4. Similarly, when a is an odd integer, a^2 ≡ 1 (mod 4) since the square of an odd number leaves a remainder of 1 when divided by 4.

Now, let's examine the expression p = a^2 * b^2. Since p is a prime number, it cannot be factored into smaller integers, except for 1 and itself. Therefore, both a and b must be either 1 or -1 modulo p. We can express this as:

a ≡ ±1 (mod p)

b ≡ ±1 (mod p)

Now, let's consider the value of p modulo 4:

p ≡ (a^2 * b^2) ≡ (±1)^2 * (±1)^2 ≡ 1 * 1 ≡ 1 (mod 4)

We know that a^2 ≡ 1 (mod 4) for any odd integer a. Therefore, both a^2 and b^2 ≡ 1 (mod 4). When we multiply them together, we still obtain the residue of 1 modulo 4.

Hence, we have proven that if p is an odd prime and p = a^2 * b^2 for integers a, b, then p ≡ 1 (mod 4).

To provide an explanation of the proof, we used the concept of quadratic residues and modular arithmetic. In modular arithmetic, numbers can be classified into different residue classes based on their remainders when divided by a given modulus. In this case, we focused on the modulus 4.

We observed that perfect squares, when divided by 4, can only have residues of 0 or 1. Specifically, the squares of even integers leave a remainder of 0, while the squares of odd integers leave a remainder of 1 when divided by 4.

Using this knowledge, we analyzed the expression p = a^2 * b^2, where p is an odd prime and a, b are integers. Since p is a prime, it cannot be factored into smaller integers, except for 1 and itself. Therefore, a and b must be either 1 or -1 modulo p.

By considering the possible residues of a^2 and b^2 modulo 4, we found that both a^2 and b^2 ≡ 1 (mod 4). When we multiply them together, the resulting product, p = a^2 * b^2, also leaves a remainder of 1 modulo 4.

Thus, we concluded that if p is an odd prime and p = a^2 * b^2 for integers a, b, then p ≡ 1 (mod 4).

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The equation that best represents y, the total cost for x gallons of gas is y = 2.12x.

The equation that best represents y, the total cost for x gallons of gas if Mrs. Masek recently filled her car with gas and paid $2.12 per gallon is :y = 2.12x

Explanation :Mrs. Masek recently filled her car with gas and paid $2.12 per gallon. Let x be the number of gallons filled in the car. Now, y can be calculated using the cost per gallon of gas and the number of gallons filled in the car. Total cost (y) = Cost per gallon × Number of gallons filled in the car. Substituting the given values, we have :y = 2.12x

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The degrees of freedom for the test is (5, 48). The p-value for this F-statistic can be obtained from an F-distribution table or calculator with the appropriate degrees of freedom.

The degrees of freedom for the regression is 5 and the sum of squares for the regression is 4,001.11. Therefore, the mean square for the regression is:

MS(regression) = SS(regression) / DF(regression) = 4,001.11 / 5 = 800.22

The degrees of freedom for the residual is 48 and the sum of squares for the residual is 2,610.04. Therefore, the mean square for the residual is:

MS(residual) = SS(residual) / DF(residual) = 2,610.04 / 48 = 54.38

The F-statistic for testing the null hypothesis that all the regression coefficients are zero is:

F = MS(regression) / MS(residual) = 800.22 / 54.38 = 14.72

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The expression ln(y) ln(z) can be condensed to ln(yz) using the product rule of logarithms. To condense the expression ln(y) ln(z) using the properties of logarithms, we can simplify it into a single logarithm expression.

1. The product rule of logarithms states that ln(a) + ln(b) is equal to ln(a * b). Applying this rule, we can rewrite the given expression as ln(yz).

2. The natural logarithm ln is a mathematical function that gives the logarithm of a number with respect to the base e. When dealing with logarithms, certain rules and properties can help simplify expressions.

3. In this case, we have ln(y) ln(z), where ln(y) and ln(z) are separate logarithmic terms. By applying the product rule of logarithms, we can combine these terms into a single logarithmic expression.

4. The product rule states that the logarithm of a product is equal to the sum of the logarithms of the individual factors. Therefore, ln(y) + ln(z) simplifies to ln(yz). This condenses the expression into a more concise form. So, the expression ln(y) ln(z) can be condensed to ln(yz) using the product rule of logarithms.

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The value of the 'x' is 3.7 units

Given a right-angle triangle, Hypotenuse is 15 units and one of the angles is 42°

To find 'x' We have to use trigonometric ratios

The cosine (cos) of an angle in a right triangle is the ratio of the length of the adjacent side to the angle to the length of the hypotenuse.

cos θ = Adjacent Side / Hypotenuse.  

From the figure, The length of the Adjacent side of the angle = x and the length of Hypotenuse = 15

cos 42° = x/15

0.74 = x/5

Multiply by 5 on both sides

5 [x/5] = 5 × 0.74

x = 3.7

Therefore, The value of the 'x' is 3.7 units

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(a) (i) For all printers P, if printer P is out of service or busy, then all print jobs are lost. (ii) There exists a print job J such that if job J is lost, then all printers are out of service. (iii) For all print jobs J, if job J is queued, then there exists a printer P that is out of service.

(b) To show they are not equivalent, we can construct a truth table and find that there is a row where they have different truth values.

(a) (i) For all printers p, if printer p is out of service or printer p is busy, then print job j is lost.

(ii) There exists a print job j such that if print job j is lost, then printer p is out of service and printer q is busy.

(iii) For all print jobs j, if print job j is queued, then there exists a printer p such that printer p is out of service.

(b) To show that ‘v’r(P(.r)) V ‘v’r(Q(Qm( ))) and ‘v’$(P($) V (2(a)) are not logically equivalent, we can construct a truth table for both statements and find that there is at least one row where the truth values differ.

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Answer:

200 students

--------------------

40% out of 500 students taking Spanish.

Find it in number:

Step-by-step explanation:

32.605    is it

Answer:

130°

Step-by-step explanation:

BC = BD

BC + BD + DC = 360°

BC + BC + 100° = 360°

2BC = (360 - 100)°

2BC = 260°

BC = 260/2

BC = 130°

Answer:

130 degrees

Step-by-step explanation:

We already know that CD = 100.

We also know that all 3 arcs in this circumscribed circle have to equal 360.

So, let's write an equation and solve for BC:

BC+CD+BD=360

BC=BD

we know this because side lengths BC and BD are congruent

(BC+BD)+100=360

we can combine like terms and substitute in our known value of CD

BC+BD=260

subtract 100 from both sides

BC+BC=260

substitute in BC=BD

2BC=260

combine like terms

BC=130

divide both sides by 2 to get BC

This means that option C (130 degrees) is correct.  Hope this helps! :)

The given series ∑ [tex](3n(4n^2))^2[/tex] converges.

To determine whether the series converges or diverges, we can use the limit comparison test.

The given series is ∑ [tex](3n(4n^2))^2[/tex], where n starts from 1 and goes to infinity.

Let's simplify the series first:

[tex](3n(4n^2))^2 = 9n^2 * 16n^4 = 144n^6[/tex]

Now, let's consider the series ∑ [tex]144n^6.[/tex]

To apply the limit comparison test, we need to find a known series ∑ [tex]b_n[/tex]that converges/diverges and has positive terms.

We can compare it with the series ∑ [tex]n^6.[/tex]

Taking the limit of the ratio of the nth terms of the two series, we have:

lim (n → ∞)[tex](144n^6 / n^6)[/tex] = 144

Since the limit is a finite positive number (144), we can conclude that if the series ∑ [tex]n^6[/tex] converges, then the series ∑[tex]144n^6[/tex] also converges. Similarly, if ∑ [tex]n^6[/tex] diverges, then ∑ [tex]144n^6[/tex] also diverges.

Now, we know that the series ∑ [tex]n^6[/tex] converges (it is a p-series with p = 6 > 1), therefore, by the limit comparison test, the series ∑ [tex]144n^6[/tex] also converges.

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Step-by-step explanation:

log2 (7) = x  

2^(log2(7) )  = 2^x

        7 = 2^x                   <======this may be what you want

when working with categorical data, a bar chart is the most appropriate choice to effectively communicate and compare the frequencies or proportions of different categories.

A bar chart is a visual representation of data using rectangular bars. It is commonly used to display and compare categorical data. Categorical data consists of distinct categories or groups that are not inherently ordered or measured numerically. Examples of categorical data include types of animals, colors, or survey responses (e.g., "Yes," "No," "Maybe").

In a bar chart, each category is represented by a separate bar, and the height of each bar corresponds to the frequency or count of observations in that category. The bars are typically arranged along the horizontal or vertical axis, making it easy to compare the frequencies or proportions of different categories.

On the other hand, numerical or continuous data refers to data that can be measured and represented on a continuous scale, such as height, temperature, or time. For such data, other types of charts, such as line graphs or histograms, may be more suitable for visualizing patterns, trends, or distributions.

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The expected value of this discrete probability distribution is 2.93, and the variance is 1.21.

To find the expected value of the discrete probability distribution for this four-sided fair die, we use the formula:

E(X) = Σ(xi * Pi)

where xi represents the possible outcomes of the die, and Pi represents the probability of each outcome. In this case, the possible outcomes are 1, 2, 3, and 4, with probabilities of 9/30, 4/30, 7/30, and 10/30 respectively.

Therefore, the expected value of X is:

E(X) = (1 * 9/30) + (2 * 4/30) + (3 * 7/30) + (4 * 10/30) = 2.93

To find the variance, we first need to calculate the squared deviations of each outcome from the expected value, which is given by:

[tex](xi - E(X))^2 * Pi[/tex]

We then sum up these values to get the variance:

[tex]Var(X) = Σ[(xi - E(X))^2 * Pi][/tex]

This calculation gives a variance of approximately 1.21.

Therefore, the expected value of this discrete probability distribution is 2.93, and the variance is 1.21.

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A month has 4 weeks, Jose's earnings for a month would be $619.8

First, let's calculate how much Jose earns in a week:

Earnings per day = $10.33/hour * 3 hours/day = $30.99/day

Weekly earnings = $30.99/day * 5 days/week = $154.95/week

Now, let's calculate the monthly earnings by multiplying the weekly earnings by the number of weeks in a month:

Monthly earnings = $154.95/week * 4 weeks/month = $619.80/month

Therefore, Jose will have $619.80 at the end of the month if he works 3 hours a day, 5 days a week, at a rate of $10.33 per hour.

At the end of the month, Jose would have earned $619.8.

As  Jose works 3 hours a day, 5 days a week, at $10.33 an hour, he would earn:

$10.33 x 3 hours a day x 5 days a week= $154.95 per week.

Since a month has 4 weeks, Jose's earnings for a month would be:

4 weeks x $154.95 per week= $619.8

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In order to solve the heat equation with non-homogeneous boundary conditions, we can use a technique known as the method of separation of variables.

Yes, that's correct. In order to solve the heat equation with non-homogeneous boundary conditions, we can use a technique known as the method of separation of variables.

This technique involves assuming that the solution to the heat equation can be written as a product of functions, each of which depends only on one of the spatial variables.

Once we have found the solution to the homogeneous heat equation with the given boundary conditions, we can subtract this solution from the solution to the non-homogeneous problem to obtain a new function that satisfies the non-homogeneous boundary conditions.

This is because the difference between the two functions satisfies the homogeneous boundary conditions, and therefore the heat equation. By applying the initial conditions to this new function, we can obtain the solution to the non-homogeneous heat equation with the given boundary conditions.

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a) For the sequence sin(1/n), as n -> infinity, 1/n -> 0, and sin(1/n) -> sin(0) = 0. Thus, the rate of convergence is O(1/n).

b) For the sequence sin(1/n^2), as n -> infinity, 1/n^2 -> 0, and sin(1/n^2) -> sin(0) = 0. Thus, the rate of convergence is O(1/n^2).

c) For the sequence (sin(1/n))^2, as n -> infinity, 1/n -> 0, and sin(1/n) -> sin(0) = 0. Thus, the rate of convergence is O(1/n^2).

d) For the sequence [ln(n+1) - ln(n)], we can use the mean value theorem to show that ln(n+1) - ln(n) = 1/(c_n+1) for some c_n between n and n+1. Thus, as n -> infinity, c_n -> infinity, and 1/(c_n+1) -> 0. Thus, the rate of convergence is O(1/n).

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The answer is C. Not enough information to determine.

To understand which stock should have the highest expected return, we need more information about the stocks and the market. Standard deviation and beta are risk measures but do not directly provide information about expected return.
Standard deviation measures the dispersion of a stock's returns, with a higher standard deviation indicating greater volatility. Beta measures a stock's sensitivity to market movements, with a higher beta indicating greater responsiveness to market changes.
While risk and return are often positively correlated, meaning that higher risk investments typically offer higher potential returns, we cannot determine the expected return of these stocks based solely on their standard deviation and beta values. We would need additional information about the stocks, such as their historical returns or dividend yields, as well as the overall market conditions, to make an informed decision on which stock has the highest expected return.

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False. A raster data model is not necessarily a better representation of reality compared to the vector model.

The statement is false. The choice between a raster data model and a vector data model depends on the specific use case and the nature of the data being represented. While raster data models are well-suited for representing continuous data, such as elevation or satellite imagery, they can be limited in accurately representing discrete objects, such as roads or buildings.

Vector data models, on the other hand, excel at representing discrete objects with precise boundaries and attributes. The accuracy and precision of points, lines, and polygons in a vector model make it a suitable choice for many applications, including cartography, urban planning, and transportation analysis. Ultimately, the choice between the two models depends on the specific requirements and characteristics of the data being represented.

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When choosing between two unbiased estimators of a population parameter, the one with the lower standard deviation is generally preferred as it indicates that the estimator is more precise. The correct answer is option d.

In other words, the variance of the estimator is smaller, meaning that the estimator is less likely to deviate far from the true value of the population parameter.

An estimator with a larger standard deviation, on the other hand, is less precise and is more likely to produce estimates that are farther from the true value. Therefore, it is important to consider the variability of the estimators when choosing between them.

It is worth noting, however, that the standard deviation alone is not sufficient to fully compare and evaluate two estimators. Other properties such as bias, efficiency, and robustness must also be taken into account depending on the specific context and requirements of the problem at hand.

The correct answer is option d.

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