(1 point) The bivariate distribution of X and Y is described below: X Y 12 10.290.49 20.110.11 A. Find the marginal probability distribution of X. 1: 2: B. Find the marginal probability distribution o

When two lines are graphed on a coordinate plane, they can be either parallel, perpendicular, or neither. Here's how to determine if slopes are for parallel lines, perpendicular lines, or neither:Slopes of Parallel LinesParallel lines have the same slope.

If two lines have slopes that are the same or equal, the lines are parallel. The slope-intercept equation for a line is y = mx + b. Where m represents the slope of the line and b represents the y-intercept.Slopes of Perpendicular LinesPerpendicular lines have slopes that are negative reciprocals of each other. The product of the slopes of two perpendicular lines is -1.

This is because the negative reciprocal of any non-zero number is the opposite of its reciprocal. In other words, if you flip a fraction, the numerator becomes the denominator and vice versa, then multiply the result by -1.To summarize, two lines are parallel if they have the same slope, perpendicular if their slopes are negative reciprocals of each other, and neither parallel nor perpendicular if their slopes are neither equal nor negative reciprocals of each other.

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A 99% confidence interval for the population mean daily vaccinations is [1154.06, 1245.94].

We can use the formula for a confidence interval for the population mean:

Confidence Interval = sample mean ± (critical value) * (standard error)

Where:

sample mean = 1200 (given)

critical value is obtained from a t-distribution table with n-1 degrees of freedom and the desired level of confidence. For a 99% confidence level with 89 degrees of freedom, the critical value is approximately 2.64.

standard error = population standard deviation / sqrt(sample size). In this case, standard error = 210 / sqrt(90) = 22.16.

Plugging in these values, we get:

Confidence Interval = 1200 ± 2.64 * 22.16

Confidence Interval = [1154.06, 1245.94]

Therefore, a 99% confidence interval for the population mean daily vaccinations is [1154.06, 1245.94].

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#24: The "D" in the cell name D25 refers to the column identifier in Excel.

#32: To enter a cell address in Excel, specifically for cell D3, when you want the row to always remain the same, you use the dollar sign ($) before the row number. So, the correct way to enter the cell address D3 while keeping the row fixed is "$D$3". By adding the dollar sign before both the column letter and the row number, the cell reference becomes an absolute reference, meaning it will not change when copied or filled down to other cells.

This is useful when you want to refer to a specific cell in formulas or when creating structured references in Excel tables.

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The function must satisfy f(0) = f(10), which is true. By Rolle's theorem, there exists a number c in (0, 10) such that f'(c) = 0. We have found that f'(x) = (x-10)(3x-10), which equals 0 at x = 10/3 and x = 10. But 10/3 is not in [0, 10]. Therefore, the only point guaranteed to exist by Rolle's Theorem is x = 10.

To determine whether Rolle's theorem applies to the given function f(x)=x(x-10)^2 on the given interval [0, 10] and to find the point(s) that are guaranteed to exist by Rolle's theorem. Rolle's Theorem states that if a function f is continuous on a closed interval [a, b] and differentiable on the open interval (a, b) and f(a) = f(b), then there exists a number c in (a, b) such that f'(c) = 0.

Therefore, the function must be continuous on the interval [0, 10] and differentiable on the open interval (0, 10).The function f(x) = x(x-10)^2 is continuous on the interval [0, 10] and differentiable on the open interval (0, 10). Therefore, Rolle's Theorem applies to the given function on the interval [0, 10].Now, we can apply Rolle's Theorem and find the point(s) that are guaranteed to exist by it.

Therefore, f'(x) = 0 at x= 10/3 or x = 10. But, 10/3 is not in the interval [0, 10]. Hence, the only point guaranteed to exist by Rolle's Theorem is x = 10.

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To determine the values of x for which the series converges, we need to analyze the behavior of the sequence (x - 5)^n as n approaches infinity.

For the series to converge, the sequence must approach zero as n goes to infinity. This means that |x - 5| < 1 for convergence.

If |x - 5| < 1, it implies that -1 < x - 5 < 1. Adding 5 to all sides of the inequality, we get:

-1 + 5 < x - 5 + 5 < 1 + 5

4 < x < 6

Therefore, the series converges for all values of x within the interval (4, 6) in interval notation.

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We need to find the volume of the region bounded by y = 3x - x² and y = 0 rotated about the vector  y-axis.

the formula for rotating about the y-axis. The formula for rotating about the y-axis is as follows:V = ∫ 2π (radius) (height) dxLet's proceed with the given problem.The given curves are:y = 3x - x²y = 0We need to find the limits of x to use in the formula for rotating about the y-axis:0 = 3x - x²x² - 3x = 0x (x - 3) = 0x = 0, 3

The limits of x are 0 and 3.The radius is x.The height is y = 3x - x².We need to substitute the value of y as x + y.So, y = 3x - x² becomes y = x(3 - x)Substituting the value of y, we get the following:V = ∫ 2πx(3 - x) dxIntegrating this using the limits x = 0 to x = 3, we get:V = 9π cubic unitsTherefore, the volume of the region bounded by y = 3x - x² and y = 0 rotated about the y-axis is 9π cubic units.

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The solution set to the simultaneous inequality 16 · x - 80 · x < 37 + 27 is x > - 1. (Correct choice: C)

In this question we find the case of a simultaneous inequality, whose solution set must be found, that is, a solution of the form x > a, where a is a real number. First, write the entire inequality:

16 · x - 80 · x < 37 + 27

Second, solve the inequality by algebra properties:

- 64 · x < 64

64 · x > - 64

x > - 1

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The probability of getting a 10 or a Jack when drawing one card from a deck is 2/13. Option (b) is the correct answer.

In a standard deck of 52 playing cards, there are 4 10s (one each of hearts, diamonds, clubs, and spades) and 4 Jacks (one each of hearts, diamonds, clubs, and spades).

The total number of favorable outcomes (getting a 10 or a Jack) is 4 + 4 = 8.

Since there are 52 cards in total, the probability of drawing a 10 or a Jack is:

P(10 or Jack) = Number of favorable outcomes / Total number of outcomes

= 8 / 52

To express this fraction in its lowest terms, we can simplify it by dividing both the numerator and denominator by their greatest common divisor, which is 4:

P(10 or Jack) = 8 / 52 = 2 / 13

Therefore, the probability of getting a 10 or a Jack when drawing one card from a deck is 2/13. Option (b) is the correct answer.

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Let’s begin with the relation R. Relation R on the set of all people is reflexive, antisymmetric, and transitive, but not symmetric.

If the relation R were symmetric, then that would imply that if a is taller than b, then b is taller than a as well. This does not hold true always. In other words, just because a is taller than b does not mean that b is taller than a too.Relation R is reflexive since each person is at least as tall as themselves. This means a relation R is formed by all the pairs (a, a) with a∈ all people.Relation R is transitive as well. If a is taller than b and b is taller than c, then a must be taller than c too. This means if (a, b) and (b, c) both belong to the relation R, then (a, c) also belongs to the relation R. This forms a cycle of height between the individuals.Relation R is also antisymmetric. If a is taller than b, then b cannot be taller than a. It implies that whenever (a, b) and (b, a) both belong to the relation R, a must be equal to b. If a person is as tall as someone else, then he/she cannot be taller than that person.Relation R is formed by all the pairs (a, b) with a, b∈ all people such that a is taller than b. Let’s determine the properties of relation R.R is not symmetric. Since the relation R is formed only by the people who are taller than others, just because a is taller than b does not mean that b is taller than a as well.R is reflexive. Each person is at least as tall as themselves. This means a relation R is formed by all the pairs (a, a) with a∈ all people.R is transitive. If a is taller than b and b is taller than c, then a must be taller than c too. This means if (a, b) and (b, c) both belong to the relation R, then (a, c) also belongs to the relation R. This forms a cycle of height between the individuals.R is antisymmetric. If a is taller than b, then b cannot be taller than a. It implies that whenever (a, b) and (b, a) both belong to the relation R, a must be equal to b. If a person is as tall as someone else, then he/she cannot be taller than that person.

Relation R is reflexive, antisymmetric, and transitive, but not symmetric.

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The given data with a Sample mean of 30, a sample standard deviation of 4.1, a sample size of 20, and SSxx of 67, the 90% confidence interval is ($28.418, $31.582), and the 95% confidence interval is ($28.083, $31.917).

To construct a confidence interval for the population mean, we need to use the formula:

Confidence interval = sample mean ± margin of error

First, let's calculate the sample mean (Ĵ₁), which is given as 30.

Next, we need to calculate the standard error (SE) using the formula:

SE = s / √n

Where s is the sample standard deviation and n is the sample size.

Given that s = 4.1 and n = 20, we can calculate the standard error:

SE = 4.1 / √20 ≈ 0.917

To calculate the margin of error, we need to determine the critical value associated with the desired confidence level. For a 90% confidence level, the critical value can be obtained from a t-table or calculator. Since the sample size is small (n < 30), we use a t-distribution instead of a normal distribution.

For a 90% confidence level with 20 degrees of freedom, the critical value is approximately 1.725.

Now, we can calculate the margin of error:

Margin of error = critical value * standard error

                = 1.725 * 0.917

                ≈ 1.582

Now we can construct the 90% confidence interval:

Confidence interval = sample mean ± margin of error

                   = 30 ± 1.582

                   ≈ (28.418, 31.582)

Thus, the 90% confidence interval for $₁ is approximately ($28.418, $31.582).

To construct a 95% confidence interval, the process is the same, but we need to use the appropriate critical value. For a 95% confidence level with 20 degrees of freedom, the critical value is approximately 2.086.

Using the same formula as above, the margin of error is:

Margin of error = 2.086 * 0.917

              ≈ 1.917

So, the 95% confidence interval is:

Confidence interval = sample mean ± margin of error

                   = 30 ± 1.917

                   ≈ (28.083, 31.917)

Therefore, the 95% confidence interval for $₁ is approximately ($28.083, $31.917).the given data with a sample mean of 30, a sample standard deviation of 4.1, a sample size of 20, and SSxx of 67, the 90% confidence interval is ($28.418, $31.582), and the 95% confidence interval is ($28.083, $31.917).

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The average depth of the water in tank B over the interval 0 < t < 10 can be found by evaluating the definite integral of the function g(t) = 3.2 + 17.5√(sin (0.16t)) divided by the length of the interval.

The average depth is the total depth divided by the time duration.

To determine whether this value is greater or less than the average depth of the water in tank A over the interval 0 ≤ t ≤ 10, we would need to have information about the model or function that represents the depth of water in tank A. Without that information, we cannot compare the two average depths.

Regarding the depth of water in tank B at time t = 6, we can evaluate the derivative of the function g(t) with respect to t and examine its sign. If the derivative is positive, the depth is increasing, and if it is negative, the depth is decreasing. The reasoning behind this is that the derivative gives the rate of change of the function.

However, the equation or model for tank B is not provided in the question, so it is not possible to determine whether the depth of water in tank B is increasing or decreasing at time t = 6 without additional information.

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The probability that a person has the disease given the result of the screening is positive is approximately 0.0162.

The probability that a person has the disease given the result of the screening is positive can be calculated using Bayes’ Theorem.

Bayes’ Theorem states that the probability of an event (A), given that another event (B) has occurred, can be calculated using the following formula:

[tex]$$P(A | B) = \frac{P(B | A) P(A)}{P(B)}$$[/tex]

where,

$$P(A | B)$$

is the probability of event A occurring given that event B has occurred, $$P(B | A)$$

is the probability of event B occurring given that event A has occurred,

$$P(A)$$

is the prior probability of event A occurring, and

$$P(B)$$

is the prior probability of event B occurring.

Using the given information, we can calculate the required probability as follows: Given,

[tex]$$P(B | A) = 0.90$$ (sensitivity)$$P(B' | A') = 0.95$$ (specificity)$$P(A) = 0.001$$$$P(A') = 1 - P(A) = 0.999$$[/tex]

We want to find

$$P(A | B)$$.

Using Bayes’ theorem, we can write:

[tex]$$P(A | B) = \frac{P(B | A) P(A)}{P(B | A) P(A) + P(B | A') P(A')}$$$$= \frac{0.90 \cdot 0.001}{0.90 \cdot 0.001 + 0.05 \cdot 0.999}$$$$≈ 0.0162$$[/tex]

Therefore, the probability that a person has the disease given the result of the screening is positive is approximately 0.0162.

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The per capita growth rate (r) is 0.05 and the new population size after one year will be 220. The correct option is D.

The per capita growth rate is the rate of population growth on a per-individual basis, and it is determined as the difference between the birth rate and the death rate, divided by the original population size. For example, the formula for calculating the per capita growth rate is: r = (B - D) / N where B is the birth rate, D is the death rate, and N is the original population size. Substituting the provided values in the formula1.mml, the per capita growth rate would be: r = (20 - 10) / 200 = 0.05So, the per capita growth rate is 0.05.

After that, the new population size after one year can be calculated using the following formula: Nt = N0 + rN0, where N0 is the original population size, Nt is the new population size, and r is the per capita growth rate. Substituting the values in the formula: Nt = 200 + (0.05 x 200) = 200 + 10 = 210So, the new population size after one year will be 220.

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The quantity theory of money is an economic theory that suggests a direct relationship between the money supply (M) and the price level (P) in an economy

According to this theory, the equation MV = PY holds, where V represents the income velocity of money and Y represents real output. This equation states that the total value of money spent in an economy (MV) is equal to the total value of goods and services produced (PY).

The quantity theory of money assumes that the velocity of money (V) and real output (Y) are relatively stable over time and that changes in the money supply (M) primarily affect changes in the price level (P). It implies that an increase in the money supply will lead to inflation, as there is more money chasing the same amount of goods and services.

Therefore, the correct answer is "static quantity theory of money," which refers to the idea that the relationship between money, velocity, price level, and real output is static and can be represented by the equation MV = PY.

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If we consider the series given by n = 1/n, we can rewrite it as follows:

n = 1/1 + 1/2 + 1/3 + 1/4 + ...

To determine the value of p for each series, we can compare it to known series forms. In this case, it resembles the harmonic series, which has the form:

1 + 1/2 + 1/3 + 1/4 + ...

The harmonic series is a p-series with p = 1. Therefore, in this case:

P1 = 1

Since the series in question is similar to the harmonic series, we know that if P1 ≤ 1, the series is divergent. Therefore, the series is divergent.

In summary:

P1 = 1 (smaller value)

P2 = N/A (not applicable)

The series is divergent.

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The value of the probabilities are:

(a) P(X > 0) = 1/2

(b) P(0 < X < 1) = 1/2

We have,

To compute the probabilities, we need to integrate the density function over the given intervals.

(a) P(X > 0):

To find P(X > 0), we need to integrate the density function f(x) = k(1 - x²) from 0 to 1:

P(X > 0) = ∫[0,1] f(x) dx

First, we need to determine the constant k by ensuring that the total area under the density function is equal to 1:

∫[-1,1] f(x) dx = 1

∫[-1,1] k(1 - x²) dx = 1

Solving the integral:

k ∫[-1,1] (1 - x²) dx = 1

k [x - (x³)/3] | [-1,1] = 1

k [(1 - (1³)/3) - (-1 - (-1)³/3)] = 1

k [(1 - 1/3) - (-1  1/3)] = 1

k (2/3 + 2/3) = 1

k = 3/4

Now we can compute P(X > 0):

P(X > 0) = ∫[0,1] (3/4)(1 - x²) dx

P(X > 0) = (3/4) [x - (x³)/3] | [0,1]

P(X > 0) = (3/4) [(1 - (1³)/3) - (0 - (0³)/3)]

P(X > 0) = (3/4) [(2/3) - 0]

P(X > 0) = (3/4) * (2/3) = 1/2

Therefore, P(X > 0) = 1/2.

(b) P(0 < X < 1):

To find P(0 < X < 1), we integrate the density function f(x) = k(1 - x²) from 0 to 1:

P(0 < X < 1) = ∫[0,1] f(x) dx

Using the previously determined value of k (k = 3/4), we can compute P(0 < X < 1):

P(0 < X < 1) = ∫[0,1] (3/4)(1 - x²) dx

P(0 < X < 1) = (3/4) [x - (x³)/3] | [0,1]

P(0 < X < 1) = (3/4) [(1 - (1³)/3) - (0 - (0³)/3)]

P(0 < X < 1) = (3/4) [(2/3) - 0]

P(0 < X < 1) = (3/4) * (2/3) = 1/2

Therefore, P(0 < X < 1) = 1/2.

Thus,

The value of the probabilities are:

(a) P(X > 0) = 1/2

(b) P(0 < X < 1) = 1/2

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The complete question:

Let X be a random variable with the density function f(x) = k(1 - x^2) for -1 ≤ x ≤ 1 and 0 elsewhere.

Compute the following probabilities:

(a) P(X > 0)

(b) P(0 < X < 1)

The convolution theorem is a technique used to simplify the multiplication of two Laplace transform functions, that is, the Laplace transform of the convolution of two functions is equal to the product of their Laplace transforms.

Consider the Laplace transform of the first function, f(t) = 3t3, which is given by F(s) = L{f(t)} = 3!/(s4). Likewise, the Laplace transform of the second function g(t) = 2t is given by G(s) = L{g(t)} = 2/(s2).Using the convolution theorem, we have the following relationship: L{f(t)*g(t)} = F(s)*G(s)where * denotes convolution of the two functions.

Hence, L{f(t)*g(t)} = (3!/(s4)) * (2/(s2))Multiplying the two Laplace transforms, we get: L{f(t)*g(t)} = 6/(s6)Hence, f(t)*g(t) = L-1{L{f(t)*g(t)}} = L-1{6/(s6)}Taking the inverse Laplace transform of the above expression, we obtain:f(t)*g(t) = 6 t5/5, where t ≥ 0Therefore, the expression using t as the variable is:f(t)*g(t) = (6t5)/5.

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The angle m∠2 in the line is 54 degrees.

complementary angles are angles that sum up to 90 degrees. The angles m∠1 and m∠2 sum up to 90 degrees. Therefore, they are complementary angles.

Let's find the angle m∠2 as follows:

m∠1 + m∠2 = 90

m∠1 = 36 degrees

Therefore,

36 + m∠2 = 90

m∠2 = 90 - 36

m∠2 = 54 degrees

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To determine the stresses at point A in the hollow shaft, we need to consider both the twisting moment and the bending moment.

Given:

Outer diameter of the shaft (D) = 1.6 in.

Wall thickness (t) = 0.125 in.

Twisting moment (T) = [value missing]

Bending moment (M) = 2000 lb-in

To calculate the stresses, we can use the following formulas:

Shear stress due to twisting:

τ_twist = (T * r) / J

Bending stress:

σ_bend = (M * c) / I

Where:

r = Radius from the center of the shaft to the point of interest (in this case, point A)

J = Polar moment of inertia

c = Distance from the neutral axis to the outer fiber (in this case, half of the wall thickness)

I = Area moment of inertia

To find the values of J and I, we need to calculate the inner radius (r_inner) and the outer radius (r_outer):

r_inner = (D / 2) - t

r_outer = D / 2

Next, we can calculate the values of J and I:

J = π * (r_outer^4 - r_inner^4) / 2

I = π * (r_outer^4 - r_inner^4) / 4

Finally, we can substitute these values into the formulas to calculate the stresses at point A.

Regarding the maximum shear stress element, it occurs at a 45-degree angle to the shaft axis. It forms a plane that is inclined at 45 degrees to the longitudinal axis of the shaft.

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the area of an equilateral triangle with a side of 6 inches is 9√3 square inches. Hence, option b is correct. by using formula A = (√3/4) × a²A

An equilateral triangle has all three sides equal. Therefore, each angle of the triangle is 60°. Let us now proceed to calculate the area of the equilateral triangle given side length 6 inches .The formula to find the area of an equilateral triangle is,A = (√3/4) × a²Where A is the area of the triangle and a is the length of the side of the equilateral triangle. Substitute the value of a = 6 inches in the formula and calculate the area of the equilateral triangle.A = (√3/4) × a²A = (√3/4) × 6²A = (√3/4) × 36A = 9√3 square inches.

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The standard deviation for each sample are given as follows:

a) Offensive lineman: 35.5 lb.

b) Defensive lineman: 27.5 lb.

For the offense, the mean is given as follows:

Mean = (278 + 302 + 310 + 290 + 252 + 304 + 359 + 319 + 350 + 260 + 300 + 359)/12 = 306.9 lbs.

Then the sum of the differences squared is given as follows:

(278 - 306.9)² + (302 - 306.9)² + ... + (359 - 306.9)².

We take the above result, divide by the sample size, and take the square root to obtain the standard deviation of 35.5 lb.

The same procedure is followed for the players on defense.

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The height of the Gorn is approximately 209.4 cm.

To find the height of the Gorn, we need to calculate the z-score by using the standard normal distribution formula.

z = (x - μ) / σ where z = z-score

x = the height of the Gornμ

= the mean height of Gorns

= 200 cmσ

= the standard deviation of heights of Gorns = 5 cm

Now, we have to find the value of the z-score that corresponds to the 95.99th percentile.

For that, we use the standard normal distribution table.

The standard normal distribution table provides the area to the left of the z-score.

We need to find the area to the right of the z-score, which is given by:1 - area to the left of the z-score

So, the area to the left of the z-score that corresponds to the 95.99th percentile is:

Area to the left of the z-score = 0.9599

To find the corresponding z-score, we look in the standard normal distribution table and find the value of z that has an area of 0.9599 to the left of it.

We can use the z-score table to find the value of z.

Using the z-score table, the value of z that corresponds to an area of 0.9599 to the left of it is 1.88.z = 1.88

Substitute the given values of μ, σ, and z into the standard normal distribution formula and solve for x.1.88 = (x - 200) / 5

Multiplying both sides by 5, we get:9.4 = x - 200

Adding 200 to both sides, we get:x = 209.4

Therefore, the height of the Gorn is approximately 209.4 cm.

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The force of the block on the rope is 35 N and it's downward. This force is acting downwards because the block is being lowered with a downward acceleration of `2.8 m/s²` by means of a rope.Thus, the correct option is 35 N. down.

The force of the block on the rope is the force due to gravity acting on it. This force is given by

`F=mg`,

where m is the mass of the block, g is the acceleration due to gravity and F is the force due to gravity acting on the block.In this case, the block is being lowered with a downward acceleration of

`2.8 m/s²`.

The acceleration of the block is given as

`a=2.8 m/s²`.

We need to find the force of the block on the rope. The force of the block on the rope is the force due to gravity acting on the block. The mass of the block is

`5-kg`.

Therefore, we can find the force due to gravity acting on the block as follows:

F = mg = 5 kg × 9.8 m/s² = 49 N

The force of the block on the rope is `49 N`.

This force is acting downwards because the block is being lowered with a downward acceleration of `2.8 m/s²` by means of a rope.Thus, the correct option is 35 N. down.

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The probability is 0.2038.

Standard error of the mean (SEM)=σ/√n

Now, let's calculate the sample mean:μx =μ= 8.1 minutesσ/√n= 5/√16= 1.25 minutes

Therefore, the sample mean, μx= 8.1 minutes.

Standard error of the mean(SEM) = σ/√n= 5/√16= 1.25 minutes1.

The probability that the sample mean is between 7 and 8 minutes.Z1 = (x1 - μx) / SEM = (7 - 8.1) / 1.25 = -0.88Z2 = (x2 - μx) / SEM = (8 - 8.1) / 1.25 = -0.08

The probability of getting Z1 and Z2 is calculated using the standard normal table.

The table gives a value of 0.1915 for Z1 = -0.88 and a value of 0.4681 for Z2 = -0.08.

So, the probability of getting the sample mean between 7 and 8 minutes is:

0.4681 - 0.1915 = 0.2766.

Hence, the probability is 0.2766.2.

The probability that the sample mean is between 8 and 9 minutes.Z1 = (x1 - μx) / SEM = (8 - 8.1) / 1.25 = -0.08Z2 = (x2 - μx) / SEM = (9 - 8.1) / 1.25 = 0.72

The probability of getting Z1 and Z2 is calculated using the standard normal table.

The table gives a value of 0.4681 for Z1 = -0.08 and a value of 0.2643 for Z2 = 0.72.

So, the probability of getting the sample mean between 8 and 9 minutes is:

0.4681 - 0.2643 = 0.2038.

Therefore, the probability is 0.2038.

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The probability that half or less of the sample are neutrophils is approximately C, 21.48%.

To solve this problem, use the binomial distribution. The probability of success (p) is 0.60 (60% chance of selecting a neutrophil) and the sample size (n) is 15.

To find the probability that half or less of the sample are neutrophils, which means to find the cumulative probability from 0 to 7.5 (since we can't have a fraction of a white blood cell).

Using a binomial distribution calculator or a statistical software, calculate this probability.

P(X ≤ 7.5) = P(X = 0) + P(X = 1) + ... + P(X = 7) + P(X = 7.5)

P(X ≤ 7.5) = 0.000 + 0.001 + ... + 0.179 + P(X = 7.5)

Now, P(X = 7.5) represents the probability of getting exactly 7.5 neutrophils, which is not a whole number. However, in a binomial distribution, probabilities are calculated for discrete values, so make an adjustment.

Consider P(X = 7) and P(X = 8) as the probabilities surrounding 7.5, and split the probability evenly between them:

P(X = 7) = P(X = 8) = 0.179 / 2 = 0.0895

Now calculate the cumulative probability:

P(X ≤ 7.5) = 0.000 + 0.001 + ... + 0.179 + 0.0895 + 0.0895

P(X ≤ 7.5) ≈ 0.2148

Therefore, the probability that half or less of the sample are neutrophils is approximately 21.48%.

The correct answer is (C) 21.48%.

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The formula for finding z-score is given by:z-score = (X - μ)/σ

Given that mean μ = $49.75 and standard deviation σ = $13.60

Value of X = $60

z-score = (X - μ)/σ

= (60 - 49.75)/13.60

= 0.755

Therefore, the z-score for a value of $60 is 0.755.

Note: The z-score is a measure of how many standard deviations a data point is from the mean.

It tells us how much a value deviates from the mean in terms of standard deviation units. A z-score of 0 means the value is at the mean, a positive z-score means it is above the mean, and a negative z-score means it is below the mean.

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The estimated value of the standard error for the difference between the two population proportions is approximately 0.1092.

To estimate the standard error for the difference between two population proportions, you can use the following formula:

Standard Error = sqrt((p1 * (1 - p1) / n1) + (p2 * (1 - p2) / n2))

where p1 and p2 are the sample proportions, and n1 and n2 are the respective sample sizes.

In this case, you are given n1 = 60, X1 = 10, n2 = 90, and X2 = 10. To estimate the standard error, you need to calculate the sample proportions first:

p1 = X1 / n1 = 10 / 60 = 1/6

p2 = X2 / n2 = 10 / 90 = 1/9

Now, substitute these values into the formula:

Standard Error = sqrt((1/6 * (1 - 1/6) / 60) + (1/9 * (1 - 1/9) / 90))

Simplifying the expression:

Standard Error = sqrt((5/36 * 31/36) / 60 + (8/81 * 73/81) / 90)

Standard Error ≈ sqrt(0.0042 + 0.0077)

Standard Error ≈ sqrt(0.0119)

Standard Error ≈ 0.1092

Therefore, the estimated value of the standard error for the difference between the two population proportions is approximately 0.1092.

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Answer:

y = -3x - 1

Step-by-step explanation:

The slope-intercept form is y = mx + b

m = the slope

b = y-intercept

Slope = rise/run or (y2 - y1) / (x2 - x1)

Point (-1, 2) (1, -4)

We see the y decrease by 6 and the x increase by 2, so the slope is

m = -6 / 2 = -3

Y-intercept is located at (0, - 1)

So, the equation is y = -3x - 1

The marginal probability of y1 and y2 are not independent.

The given marginal probability function of y1 was derived to be binomial with n=2 and p=1/3.  To check the independence, let's compute the joint probability of y1 and y2 using the marginal probability functions of both random variables.

Let's denote the joint probability as P(y1,y2).From the given information, the probability function of y1 is P(y1=k) = (2Ck) * (1/3)^k * (2/3)^(2-k), for k=0,1,2.  (2Ck) is the binomial coefficient or combination.The probability function of y2 can also be derived in the same way as P(y2=k) = (2Ck) * (1/3)^k * (2/3)^(2-k), for k=0,1,2.The joint probability of y1 and y2 can be computed asP(y1,y2) = P(y1=k1 and y2=k2) = P(y1=k1) * P(y2=k2)For k1=0,1,2 and k2=0,1,2, P(y1,y2) can be computed using the above equation.

For instance, when k1=1 and k2=2,P(1,2) = P(y1=1) * P(y2=2) = (2C1) * (1/3) * (2/3) * (2C2) * (1/3)^2 * (2/3)^0 = 0.In general, if y1 and y2 are independent, P(y1,y2) = P(y1) * P(y2) should hold for any pair (y1,y2). However, the joint probability computed above may not always be equal to the product of marginal probabilities, which implies y1 and y2 are not independent.

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the work done by the gravity on the particle will be zero ,by using formula of work done = force x displacement

Given that a particle moves in a vertical plane along the closed path seen in the figure, starting at A and eventually returning to its starting point. We are supposed to find the work done on the particle by gravity.What is work done?Work done is a physical quantity which is defined as the product of the force applied to an object and the distance it moves in the direction of the force. The formula for work done is given by:Work done = force x distance moved in the direction of force When the particle moves in a closed path and returns to the initial position, then the net displacement is zero.

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