Help look in the image below!

According to the figure in the problem, the option that best describes it is

D. Not similar

This is a term used in geometry to mean that the respective sides of the triangles are proportional and the corresponding angles of the triangles are congruent

In this case as in the figure, there is a congruent and angle however the sides are not proportional

That is to say that

42 / 20 ≠ 36 / 24

21 / 10 is not equal to 3 / 2

hence the sides are not proportional

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The lateral area of the parallelogram prism is,

⇒ 0.048 m²

Since, An equation is an expression that shows the relationship between two or more numbers and variables.

Given that;

the lateral area of the right prism has height 0.3dm and the base of the prism is a parallelogram with sides 6cm and 20mm.

Now, We have;

0.3 dm = 0.3 m,

6 cm = 0.06 m,

20 mm = 0.02 m,

Hence:

The lateral area of the right prism is given by:

Lateral area = 2(0.06 x 0.3) + 2(0.02 x 0.3)

Lateral area = 0.048 m²

So, The lateral area of the parallelogram prism is 0.048 m²

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The eigenvalues and eigenvectors of a Markov matrix are important in understanding the long-term behavior of a system. Let's first find the eigenvalues and eigenvectors of the given Markov matrices.

For matrix A, the characteristic equation is:

|λI - A| = 0

where I is the identity matrix. Solving for λ, we get:

(λ - 1)(λ - 0.5)(λ + 0.5) = 0

So the eigenvalues are λ1 = 1, λ2 = 0.5, and λ3 = -0.5.

Next, we can find the eigenvectors associated with each eigenvalue by solving the system of equations:

(A - λI)x = 0

For λ1 = 1, we have:

A - I =

[0.5 0.2 0.3]

[0.1 0.7 0.2]

[0.2 0.1 0.7]

Reducing to row echelon form, we get:

[1 0.4 0.6]

[0 1 -0.2857]

[0 0 0 ]

So the eigenvector associated with λ1 is:

[0.6]

[-0.2857]

[1 ]

Similarly, for λ2 = 0.5, we get the eigenvector:

[1]

[-1]

[0]

And for λ3 = -0.5, we get the eigenvector:

[-0.6]

[-0.5714]

[1 ]

For matrix A_00, the process is the same. The eigenvalues are λ1 = 1, λ2 = 0.6, and λ3 = 0.3. The corresponding eigenvectors are:

λ1: [0.6, -0.4, 0.7]

λ2: [0.8, 0.5, -0.3]

λ3: [-0.1, -0.75, -0.65]

Now, let's consider why A_100 is close to A_00. We can use the fact that A_100 = A^n, where n is the number of transitions in the Markov process. As n gets larger, the behavior of the system approaches the steady state, which is represented by the eigenvector associated with the eigenvalue of 1.

Since the eigenvalue of 1 is common to both A and A_00, we can see that the long-term behavior of both systems is governed by the same eigenvector. Therefore, as n gets larger, the difference between A_100 and A_00 becomes smaller, and the systems approach the same steady state behavior.

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The area of the shaded region is 6.261 square units. To find the area of the shaded region, we need to first find the points of intersection of the given curves.

The curves intersect at y = ln(7) and y = -1. We can then use the formula for finding the area between two curves:

A = ∫[a,b] (f(x) - g(x)) dx

where a and b are the x-coordinates of the points of intersection, and f(x) and g(x) are the equations of the curves. Evaluating this integral gives us the area of the shaded region as 6.261 square units.

To understand this better, we can plot the given curves and the shaded region using a graphing calculator or software. The region is bound by the curves y = 1, y = e^x, and y = x^2 - 7. By visually inspecting the graph, we can see that the region is a combination of two regions: a triangular region with base 1 and height ln(7)+1, and a curved region bounded by y = e^x and y = x^2 - 7. We can approximate the area of the curved region using numerical methods such as the trapezoidal rule or Simpson's rule, and then add it to the area of the triangular region to get the total area of the shaded region. However, it is faster and more accurate to use the formula for finding the area between two curves as described above.

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average value, which cannot be negative. Thus, the final answer is: The average value of f(x) over the interval [1, 6] is C. 5/12.

To find the average value of f(x) over the interval [1, 6], we need to use the formula: average value of f(x) = (1/b-a) * integral from a to b of f(x) dx In this case, a = 1 and b = 6, so we have: average value of f(x) = (1/6-1) * integral from 1 to 6 of f(x) dx

To evaluate the integral, we need to find the antiderivative of f(x). Since f(x) is a constant function, its antiderivative is simply x multiplied by the constant value of f(x), which is 1/6. Thus, we have:

integral from 1 to 6 of f(x) dx = (1/6) * integral from 1 to 6 of dx = (1/6) * (6-1) = 5/6

Plugging this back into the formula for the average value of f(x), we get:

average value of f(x) = (1/6-1) * 5/6 = (-1/5) * 5/6 = -1/6

However, we need to take the absolute value of this answer since we're looking for an average value, which cannot be negative. Thus, the final answer is:

average value of f(x) = | -1/6 | = 1/6

Therefore, the correct answer is C. 5/12.

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The radius of convergence of a power series is given by the formula:  

r = 1/lim sup |an|^(1/n)

In this case, we have a power series of the form

∑ (-1)^n * xn/n^9

Using the root test, we have:

lim sup |(-1)^n * xn/n^9|^(1/n) = lim sup (|x|/n^9)^(1/n) = |x|lim sup n^(-9/n)

Note that lim sup n^(-9/n) = 1, so we have:

r = 1/lim sup |(-1)^n * xn/n^9|^(1/n) = 1/|x|

Therefore, the radius of convergence is 1/|x|.

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Statement: "Statistical errors: p values, the gold standard of statistical validity, are not as reliable as many scientists assume."

P values are a commonly used statistical measure that provides a way to determine whether the observed results of an experiment or study are statistically significant or just due to chance. A p value is the probability of obtaining results as extreme as or more extreme than the observed results, assuming that the null hypothesis (i.e., no effect) is true.

However, in recent years, there has been growing concern that p values are not as reliable as previously assumed. Some scientists argue that p values can be misleading and that they are often misinterpreted or overemphasized.

One reason for this is that p values do not provide information about effect size or the clinical relevance of the observed results. A statistically significant result may not necessarily be practically significant or meaningful in a real-world context.

Another issue is that p values are highly dependent on sample size and can be influenced by the choice of statistical test or the pre-specified significance level. This means that p values can vary widely between studies even if the underlying effect is the same.

Therefore, some scientists are calling for a shift away from relying solely on p values and advocating for a more holistic approach to statistical analysis that takes into account effect size, confidence intervals, and other measures of uncertainty.

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The angle at which their paths intersect at the airport is approximately 113.7 degrees.

We have,

To find the angle at which their paths intersect at the airport, we can use the Law of Cosines, which relates the sides and angles of a triangle:

c² = a² + b² - 2 ab cos(C)

where c is the side opposite to angle C.

Let's call the distance from the first plane to the airport "a" and the distance from the second plane to the airport "b".

The distance between the two planes.

c = 805 km

Substituting these values into the equation, we get:

805² = a² + b² - 2ab*cos(C)

Simplifying and rearranging, we get:

cos(C) = (a² + b² - c²) / 2ab

Substituting the given values, we get:

cos(C) = (322² + 513² - 805²) / (2322513)

cos(C) = -0.3805

To find the angle C, we can take the inverse cosine of -0.3805:

C = cos^{-1}(-0.3805)

C ≈ 113.7°

Therefore,

The angle at which their paths intersect at the airport is approximately 113.7 degrees.

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The fraction of the shape that is shaded is determined as 3/10.

The fraction of the shape that is shaded is the ratio of the shaded area to the total area of the shape.

The area of the shaded shape is calculated as follows;

Area = ¹/₂ x base x height

Let the height of the figure = h

Area = ¹/₂ x 12 mm x h

Area = 6h

The area of the trapezoid is calculated as follows;

Area = ¹/₂ (sum of parallel sides ) x height

Area = ¹/₂ ( 28 mm + 12 mm ) x h

Area = ¹/₂ (40 mm ) x h

Area = 20h

The fraction of the shape that is shaded is calculated as follows;

= 6h / 20h

= 6/20

= 3/10

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The expressions where the distributive property of addition is applied are:

7(4 + p) = 11 + 4p

15b + 10c = 5 (3b + 2c)

6 (3 + y) = 18 + 6y

We have,

The distributive property of addition.

a ( b + c) = ab + ac

Now,

7(4 + p) = 11 + 4p

15b + 10c = 5 (3b + 2c)

6 (3 + y) = 18 + 6y

The expressions are:

7(4 + p) = 11 + 4p

This is the distributive property of addition.

15b + 10c = 5 (3b + 2c)

This is the distributive property of addition.

a + a + a = 3a

This is the simple addition of like terms.

6 (3 + y) = 18 + 6y

This is the distributive property of addition.

b + b + b + b + b = b^5

This is the simple addition of like terms.

Thus,

The expressions where the distributive property of addition is applied are:

7(4 + p) = 11 + 4p

15b + 10c = 5 (3b + 2c)

6 (3 + y) = 18 + 6y

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The quadratic equation for the given vertex and point is:

y = -4(x + 2)² + 7

A parabola whose vertex is (h, k) and that has a leading coefficient a, can be written in the vertex form as:

y = a*(x - h)² + k

Here we know that the vertex is (-2, 7), then we can write the equation as:

y =  a*(x + 2)² + 7

We also know that the parabola passes through (-1, 3), then we can replace these two values in the equation to get:

3 = a*(-1 + 2)² + 7

3 = a + 7

3 - 7 =a

-4 = a

Then the equation for the parabola is:

y = -4(x + 2)² + 7

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The exact length of the polar curve r = 5cos(theta) over the interval 0 ≤ theta ≤ pi/2 is 5π/2 units

To find the length of the polar curve r = 5cos(theta) over the interval 0 ≤ theta ≤ pi/2, we can use the following formula:

L = ∫[a,b] sqrt[r^2 + (dr/dθ)^2] dθ

where a and b are the initial and final values of theta over the interval, and r and (dr/dθ) are the polar equation and its derivative with respect to theta.

First, we need to find the derivative of r with respect to theta:

dr/dθ = -5sin(theta)

Then, we can substitute r and (dr/dθ) into the formula:

L = ∫[0,π/2] sqrt[(5cos(theta))^2 + (-5sin(theta))^2] dθ

Simplifying under the square root:

L = ∫[0,π/2] sqrt[25(cos^2(theta) + sin^2(theta))] dθ

L = ∫[0,π/2] 5 dθ

L = 5θ ∣ [0,π/2]

L = 5(π/2 - 0)

L = 5π/2

Therefore, the exact length of the polar curve r = 5cos(theta) over the interval 0 ≤ theta ≤ pi/2 is 5π/2 units.

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The probability of drawing a white ball will then be 12/24 = 1/2 or 0.5.After adding 6 more white balls, there will be a total of 24 balls in the bag, with 24/2 = 12 white balls.

i) The probability of drawing a white ball can be found by dividing the number of white balls in the bag by the total number of balls in the bag. Since there are x white balls out of 12 total balls, the probability of drawing a white ball is x/12.

(ii) If 6 more white balls are added to the bag, the total number of white balls becomes x+6, and the total number of balls in the bag becomes 12+6=18. The probability of drawing a white ball is now twice the probability in (i), which can be written as:

2(x/12) = (x+6)/18

Solving for x gives:

2x = (x+6)(3/2)

4x = 3x+18

x = 18

Therefore, there are originally 18 white balls in the bag, and the probability of drawing a white ball is 18/12 = 3/2 or 0.25.

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If you select only the top 10% of the eigenvectors for dimensionality reduction, the dimensions of the projected data sample matrix would be the number of observations (rows) times the reduced number of features (columns), which is equal to 10% of the original number of features.

The number of eigenvectors and eigenvalues obtained via decomposition of the sample covariance matrix is equal to the number of features in the data sample. So if the data sample has n features, then we would obtain n eigenvectors and eigenvalues.
If we select only the top 10% of the eigenvectors for dimensionality reduction, then the dimensions of the projected data sample matrix would be reduced by 90%, meaning it would have only 10% of the original number of features. For example, if the original data sample had 100 features, then selecting only the top 10% of eigenvectors would result in a projected data sample matrix with only 10 features.
When performing eigendecomposition on a sample covariance matrix of a data sample containing features and observations, you will obtain the same number of eigenvectors and eigenvalues as the number of features in your dataset.
If you select only the top 10% of the eigenvectors for dimensionality reduction, the dimensions of the projected data sample matrix would be the number of observations (rows) times the reduced number of features (columns), which is equal to 10% of the original number of features.

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The length of the curve over the given interval is 2πa, where a is the constant in the polar equation r = 8a cos θ.

To find the length of the curve defined by a polar equation, we use the formula:

L = ∫[tex]a^b[/tex] √[r² + (dr/dθ)²] dθ

where a and b are the angles of the interval and r is the polar equation.

In this case, r = 8a cos θ, so we need to find dr/dθ:

dr/dθ = -8a sin θ

Now we can substitute into the formula for L:

L = ∫[tex](-\pi /16)^(^\pi^ /^1^6^)[/tex] √[(8a cos θ)²+ (-8a sin θ)²] dθ

Simplifying under the square root:

L = ∫[tex](-\pi /16)^(^\pi ^/^1^6^)[/tex] √[64a²(cos²θ + sin²θ)] dθ

L = ∫[tex](-\pi /16)^(^\pi^ /^1^6^)[/tex] 8a dθ

L = 16a(π/16 - (-π/16))

L = 2πa

Therefore, the length of the curve over the given interval is 2πa, where a is the constant in the polar equation r = 8a cos θ.

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To calculate the standard error of the estimate (SE), we need to use the Sum of Squares Residual (SSE) and the Total Sum of Squares (SST). The formula for SE is as follows:

SE = sqrt(SSE / (n - k))

Where:

SSE is the Sum of Squares Residual,

n is the number of observations (in this case, 27 cities),

k is the number of predictors (independent variables).

In this case, we don't have the number of predictors (k), but we can assume it as 2 since the sociologist studies the crime rate as a function of poverty rate and median income.

Using the given information:

SSE = 4183854

SST = 7684380

n = 27

k = 2

SE = sqrt(SSE / (n - k))

= sqrt(4183854 / (27 - 2))

= sqrt(4183854 / 25)

= sqrt(167354.16)

≈ 408.28

Rounded to 4 decimal places, the standard error of the estimate is approximately 408.28.

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No, we cannot conclude that the series [infinity] cn(−2)n n = 0 is convergent, even if [infinity] cn3n n = 0 is convergent.

The convergence of a power series at a point x = a is determined by the values of the coefficients cn and the distance between x and a. If [infinity] cn3n n = 0 is convergent, then the radius of convergence of the power series is at least 1/3, which means the power series converges for |x| < 1/3.

However, the convergence of the series [infinity] cn(−2)n n = 0 cannot be determined by the convergence of [infinity] cn3n n = 0. The radius of convergence of the series [infinity] cn(−2)n n = 0 may be smaller than 1/3, larger than 1/3, or even infinite.

Therefore, we need to test the convergence of [infinity] cn(−2)n n = 0 separately.

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The prevalence of undernutrition among children aged 0-12 years in Refugee Camp X on January 1, 2011, was 57%. The incidence rate of measles among children during the same period was 51%.

The prevalence of undernutrition on January 1, 2011, can be calculated by dividing the number of children diagnosed with undernutrition (80) by the total number of children (140) and multiplying by 100. Therefore, (80/140) * 100 = 57%. This means that 57% of children in Refugee Camp X were diagnosed with undernutrition on January 1, 2011.

On January 31, 2011, there were no new additions or deaths among the children aged 0-12 years. Therefore, the total number of children remained the same at 140. Since the number of children with undernutrition did not change, the prevalence of undernutrition remained at 57%.

The incidence rate of undernutrition during the month of January 2011 can be calculated by dividing the number of new cases (20) by the total number of children at the beginning of the month (140) and multiplying by 100. So, (20/140) * 100 = 14%. This means that 14% of children developed undernutrition during January.

The incidence rate of measles during the month of January 2011 can be calculated by dividing the number of new measles cases (72) by the total number of children at the beginning of the month (140) and multiplying by 100. Thus, (72/140) * 100 = 51%. This indicates that 51% of children in Refugee Camp X developed measles during January 2011.

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On Monday the total number of sold hamburgers are 118.

Let's call the number of hamburgers sold "h" and the number of cheeseburgers sold "c".

From the problem, we know two things:

The total number of burgers sold is 472:

h + c = 472

The number of cheeseburgers sold is three times the number of hamburgers sold:

c = 3h

We can use substitution to solve for h:

h + 3h = 472

4h = 472

h = 118

Therefore, 118 hamburgers were sold on Monday.

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The answer to this question is c.The use of a correlation coefficient.

The use of a correlation coefficient would tell you that an association claim is being made in a research study. A correlation coefficient is a statistical measure that shows the strength and direction of a relationship between two variables. It ranges from -1 to 1, with 0 indicating no relationship and -1 or 1 indicating a perfect negative or positive relationship, respectively.

A scatterplot or bar graph may be used to visually display the relationship between two variables, but they do not necessarily indicate that an association claim is being made. The measurement of two variables is necessary for any type of research study, but it does not inherently imply that an association claim is being made.

Interrogation of internal validity is a process used to ensure that the study's results accurately reflect the relationship between the variables being studied. It is important for any research study but does not specifically indicate that an association claim is being made.

Therefore,the answer to this question is c.The use of a correlation coefficient.

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Por lo tanto, el valor de 72 + 68 - 59 es 81.

Para encontrar el valor de la expresión 72 + 68 - 59, primero necesitamos determinar el valor de "a" en la ecuación dada.

Dado que "ax98" es un número impar y "ax99" también es impar, podemos concluir que "a" debe ser un número impar. Supongamos que "a" es igual a algún número impar "x".

Ahora podemos sustituir el valor de "a" en la expresión 72 + 68 - 59:

72 + 68 - 59 = 140 - 59 = 81

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The gradient vector ∇f(x, y) is given by (∂f/∂x, ∂f/∂y). Thus, for f(x, y) = xy, we have ∇f(x, y) = (y, x). Evaluating this at (5, 7), we get ∇f(5, 7) = (7, 5).

The tangent line to the level curve f(x, y) = 35 at the point (5, 7) is perpendicular to the gradient vector ∇f(5, 7) and passes through (5, 7). Since the gradient vector ∇f(5, 7) = (7, 5) is perpendicular to the tangent line, the tangent line must have a slope of -7/5 (the negative reciprocal of 7/5). Thus, the equation of the tangent line is y - 7 = (-7/5)(x - 5), which simplifies to y = (-7/5)x + 56/5.

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The problem asks us to determine the percentage of 20-foot shipping containers that weigh less than 23,310 pounds,

Given that the weight of these containers follows a normal distribution with a mean of 27,000 pounds and a standard deviation of 3,000 pounds.

To solve this problem, we can use the standard normal distribution and convert the value of 23,310 pounds to a z-score using the formula z = (x - μ) / σ, where x is the value we want to find the probability for, μ is the mean, and σ is the standard deviation.

In this case, the z-score for 23,310 pounds is -1.23.

Using a standard normal distribution table or calculator, we can find that the probability of a z-score less than -1.23 is approximately 0.1103. This means that the percentage of 20-foot shipping containers that weigh less than 23,310 pounds is approximately 11.03%.

This result has practical applications for industries that rely on shipping containers for transporting goods.

By knowing the probability of a container weighing less than a certain amount, companies can make informed decisions about how much they should pack in each container and how many containers they need for a particular shipment.

This can help them optimize their logistics and reduce costs associated with over-packing or under-packing containers.

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Answer:

[tex]\huge\boxed{\sf f^{-1}(x)=\frac{x}{2} + 1}[/tex]

Step-by-step explanation:

f(x) = 2(x - 1)

Put f(x) = y

y = 2(x - 1)

Exchange x and y

x = 2(y - 1)

Now, solve for y:

x = 2(y - 1)

Divide both sides by 2

[tex]\displaystyle \frac{x}{2} = y - 1[/tex]

Add 1 to both sides

[tex]\displaystyle \frac{x}{2} + 1 = y[/tex]

Put y = f⁻¹(x)

[tex]\displaystyle \frac{x}{2} + 1 = f^{-1}(x)[/tex]

OR

[tex]\displaystyle f^{-1}(x)=\frac{x}{2} + 1 \\\\\rule[225]{225}{2}[/tex]

Answer: 108

Step-by-step explanation: 24 copies x 4 minutes = 96

24/2 = 12 copies/ 30 seconds

96+12 = 108

:)

Answer:

108 copies

Step-by-step explanation:

We Know

A copy machine makes 24 copies per minute.

How many copies does it make in 4 minutes and 30 seconds?

Let's solve

4 minutes and 30 seconds = 4.5 minutes

We Take

24 x 4.5 = 108 copies

So, it make 108 copies in 4 minutes and 30 seconds.

a. The initial size of the bacteria colony is 1 gram.

b. The growth rate for this bacteria colony is 25% per day.

c. The size of the bacteria colony after t days is given by the equation S(t) = 1 * e^(0.25t) grams.

d. To find when the colony size reaches 5 grams, we need to solve the equation 5 = 1 * e^(0.25t) for t. Taking the natural logarithm of both sides and rearranging, we get t = ln(5)/0.25 ≈ 8.7 days.

e. The doubling time for this colony is approximately 2.77 days, which is found by solving the equation 2 = 1 * e^(0.25t) for t. Taking the natural logarithm of both sides and rearranging, we get t = ln(2)/0.25 ≈ 2.77 days.

The law of uninhibited growth states that the growth rate of a population is proportional to its size, and there are no limiting factors that affect the growth. In this problem, the size of the bacteria colony is modeled using an exponential function, where the initial size of the colony is 1 gram and the growth rate is 25% per day.

To find the size of the colony after a certain number of days, we can substitute the given value of t into the equation S(t) = 1 * e^(0.25t). For example, after 5 days, the size of the colony is S(5) = 1 * e^(0.25*5) ≈ 2.28 grams.

To find when the colony size reaches a certain value, we need to solve the exponential equation S(t) = 1 * e^(0.25t) = A, where A is the desired size. Taking the natural logarithm of both sides and rearranging, we get t = ln(A)/0.25. For example, to find when the colony size reaches 5 grams, we solve the equation 5 = 1 * e^(0.25t) for t and get t = ln(5)/0.25 ≈ 8.7 days.

The doubling time is the time it takes for the population to double in size. In this case, we need to solve the exponential equation 2 = 1 * e^(0.25t) for t. Taking the natural logarithm of both sides and rearranging, we get t = ln(2)/0.25 ≈ 2.77 days.

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Probit regression is a technique that uses the Gaussian (normal) distribution as its underlying model. The choice between logistic regression and probit regression often depends on the specific application and the assumptions made about the distribution of the dependent variable.

The logistic distribution assumes a constant odds ratio, whereas the Gaussian distribution assumes a linear relationship between the independent and dependent variables. However, both models can be used for binary outcomes and have similar interpretations for the coefficients.

Therefore, the decision to use logistic regression or probit regression should be based on the underlying assumptions and the research question being addressed.

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False. Forecasts based on mathematical formulas are not referred to as qualitative forecasts. Qualitative forecasts are based on subjective judgments, opinions, or expert insights rather than mathematical formulas.

These forecasts rely on qualitative data such as surveys, interviews, or expert opinions to make predictions. Qualitative forecasting techniques are often used when there is limited historical data available or when factors such as human behavior, market trends, or social factors play a significant role in the forecast. On the other hand, forecasts based on mathematical formulas are referred to as quantitative forecasts.

These forecasts use mathematical models, statistical techniques, and historical data to make predictions. Examples of quantitative forecasting methods include time series analysis, regression analysis, and exponential smoothing.

It is important to distinguish between qualitative and quantitative forecasts as they utilize different approaches and data sources to make predictions. Therefore, the statement that forecasts based on mathematical formulas are referred to as qualitative forecasts is false.

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The likelihood that each party will ask for a high chair when Hugo is serving at a restaurant is 0.03. Hugo served 10 parties in an hour. Based on this information, it is possible to calculate the probability that a certain number of parties will ask for a high chair during that hour.

To calculate the probability, we can use the binomial distribution formula. The binomial distribution is a probability distribution that describes the number of successes in a fixed number of independent trials, where each trial has the same probability of success.

In this case, we have 10 independent trials (the 10 parties that Hugo served), and the probability of success in each trial is 0.03 (the likelihood that a party will ask for a high chair). Using the binomial distribution formula, we can calculate the probability of different numbers of successes (i.e., the number of parties that ask for a high chair).

For example, the probability that no parties will ask for a high chair is (1-0.03)^10, or approximately 0.744. The probability that exactly one party will ask for a high chair is 10*(0.03)*(1-0.03)^9, or approximately 0.261. The probability that two or more parties will ask for a high chair is 1 minus the sum of the probabilities of zero and one party asking for a high chair, or approximately 0.011.

To learn more about probability click here: brainly.com/question/16259980

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