Consider the upper bound for total numerical error E h2 eh) + M h 6 Prove that e(h) has a minimum at h = : 3€/M

The standard deviation of the number of correct answers is approximately 13.96.

The number of correct answers on a true or false question when the student is guessing is a binomial random variable. The mean of this variable is the product of the number of trials and the probability of success on each trial. Since the student has a 50-50 chance of getting each question right, the probability of success is 0.5.

The mean of the number of correct answers is:

mean = number of trials × probability of success

mean = 780 × 0.5

mean = 390

The variance of the number of correct answers is the product of the number of trials, the probability of success, and the probability of failure. Since the probability of failure is also 0.5, the variance is:

variance = number of trials × probability of success × probability of failure

variance = 780 × 0.5 × 0.5

variance = 195

The standard deviation is the square root of the variance:

standard deviation = sqrt(variance)

standard deviation = sqrt(195)

standard deviation ≈ 13.96

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The total recruiting cost is $135,492.35.

In order to get the total recruiting cost, we must  add up all the expenses incurred during the hiring process.

Expenses:

The advertising expense was $12,816.40

Moving expense was $15,419

Real estate broker's fee was $38,430 (7% of $549,000).

The total cost for these expenses is $66,665.40.

The agency fee, 25% of Henry Little's first-year salary will give us an agency fee of $63,690 (254,760 * 1/4).

We will add up the travel costs for the three candidates who were interviewed.

Hakeem Golden's travel cost was $1,948.75

Nancy Cooper's was $1,516.40

Henry Little's was $1,671.80.

The total travel cost is $5,136.95.

The total recruiting cost will be:

= $66,665.40 + $63,690 + $5,136.95.

= $135,492.35

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Dr. Hellsing is in the process of establishing a norm-referenced assessment.

Norm-referenced assessment is a method that evaluates an individual's aptitude in comparison with the outputs of a larger, representative sample of test takers from the corresponding population. This technique ranks persons on a scale relative to each other instead of objectively evaluating their actual abilities.

Therefore, by giving the exam to a significant group of high school graduates, Dr. Hellsing will be able to generate standards to interpret separate test scores and equate them to the outcome of others in the same demographic.

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The probability that the product of the two rolled numbers is prime is 11/36

To determine the probability that the product of the two rolled numbers is prime, we first need to understand what a prime number is. A prime number is a positive integer greater than 1 that has no positive integer divisors other than 1 and itself. In this case, we need to consider all possible combinations of rolling a 6-sided die twice, which is 6x6 = 36 possible outcomes.

We can calculate the probability of rolling a prime product by listing all of the possible products and identifying which ones are prime. The possible products are as follows:

1, 2, 3, 4, 5, 6, 2, 4, 6, 8, 10, 12, 3, 6, 9, 12, 15, 18, 4, 8, 12, 16, 20, 24, 5, 10, 15, 20, 25, 30, 6, 12, 18, 24, 30, 36

We can see that the prime products are 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, and 31. There are 11 prime products out of a total of 36 possible outcomes. Therefore, the probability of rolling a prime product is 11/36, or approximately 0.31.

In summary, the probability that the product of the two rolled numbers is prime is 11/36.

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The decibel level of the street on a weekend with only 37 cars passing a given point every minute is approximately 64.65 dB.

The decibel level of a busy street with 126 cars passing a given point every minute is 69dB. We can use this information to estimate the change in decibel level when the number of cars is reduced to 37 cars every minute on a weekend.

First, we need to understand how changes in the number of cars passing by will affect the decibel level. The decibel level is a logarithmic measure of the intensity of sound, which means that a small change in the number of cars passing by can have a significant effect on the decibel level.

The relationship between the decibel level and the number of cars passing by can be modeled using the following formula:

L2 = L1 + 10 × log10(N2/N1)

where L1 is the decibel level with N1 cars passing by, L2 is the decibel level with N2 cars passing by, and log10 is the logarithm base 10 function.

Using the given information, we can calculate the decibel level on a weekend when only 37 cars pass a given point every minute:

L2 = 69 + 10log10(37/126)

L2 = 69 + 10log10(0.2937)

L2 = 69 + (-4.35)

L2 = 64.65 dB

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The probability that the mixture will test positive is approximately 0.4744 or 47.44%

In this blood testing procedure, the mixture will test positive if at least one of the individual samples tests positive. To determine the probability of the mixture testing positive, we can first find the probability that all individual samples test negative and then subtract that from 1.

The probability that an individual sample tests negative is 1 - 0.12 = 0.88, since there is a 0.12 chance that it tests positive. As there are 5 samples, and we assume they are independent, we can multiply the probabilities together to find the probability that all samples test negative: 0.88^5 ≈ 0.5256.

Now, to find the probability that the mixture tests positive (meaning at least one individual sample is positive), we can subtract the probability of all samples being negative from 1: 1 - 0.5256 ≈ 0.4744.

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The requreid right model is 5.5 inches tall. Option B is correct.

We can set up a proportion based on the information given:

1 in on the left model corresponds to 30 ft in real life

1 in on the right model corresponds to 15 ft in real life

Let h be the height of the right model in inches.

Then we have:

2.75/1 = h/15

Cross-multiplying, we get:

1/ h = 2.75 / 15

h = 5.5

Therefore, the right model is 5.5 inches tall. Option B is correct.

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The mean SAT Math score for all the seniors at this school is 700.

To find the mean SAT Math score of all the seniors at this school, we need to calculate the overall mean by combining the mean of the Key Society members and non-members.

We know that there are 18 seniors in the Key Society with a mean SAT Math score of 714 and 12 seniors who are not members of the Key Society with a mean SAT Math score of 679.

To calculate the overall mean, we can use the formula:

Overall Mean = (Sum of Key Society Mean + Sum of Non-Member Mean) / Total Number of Seniors

Sum of Key Society Mean = 18 * 714 = 12,852
Sum of Non-Member Mean = 12 * 679 = 8,148
Total Number of Seniors = 18 + 12 = 30

Overall Mean = (12,852 + 8,148) / 30
Overall Mean = 21,000 / 30
Overall Mean = 700

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The Median value for Brand X is 13, The Median value for Brand Y is 16, and Brand Y has a longer battery life.

The median is a measure of central tendency that represents the middle value of a dataset when it is ordered from smallest to largest (or vice versa). It is the value that separates the upper half of the data from the lower half.

Median value is depicted on a box plot by the vertical line that divides the rectangular box. Therefore

Median value for Brand X = 13

Median value for Brand Y = 16

Brand Y has a higher median value (16) than Brand X (13).

This implies that brand Y has a battery life that last longer than brand X.

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The dependent variable in this study is the number of times the students got out of their seats over the course of 2 hours.

The independent variable is the variable that is being manipulated or controlled by the researcher.

It is the variable that is expected to cause a change in the dependent variable which is the variable being measured as the outcome or response to the manipulation of the independent variable.

In the given scenario, the independent variable is the group assignment based on the scores on the sensation-seeking test.

The researcher has divided the students into two groups based on their scores, and this grouping is being used as a way of manipulating or controlling the students' level of sensation-seeking behavior.

The researcher is interested in how this manipulation affects the students' behavior in terms of getting out of their seats.

The dependent variable is the number of times the students got out of their seats over the course of 2 hours.

This variable is expected to vary depending on the level of the independent variable, which is the group assignment based on the sensation-seeking test scores.

The researcher will measure the dependent variable for each group separately and then compare the results to determine if there is a significant difference between the two groups.

In summary,

The independent variable is the grouping of the students based on their scores on the sensation-seeking test and the dependent variable is the number of times the students got out of their seats over the course of 2 hours.

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Answer:

I would say a bar graph because it is used best for data that needs groups.

In order to get samples from each age group, you would need to use stratified sampling. This involves dividing the population into subgroups, or strata, based on a particular characteristic - in this case, age.

Once the population has been stratified, a random sample can be taken from each subgroup in proportion to its size.

For example, if the population consists of 1000 people, with 300 aged 20-40, 400 aged 40-60, and 300 aged 60-80, you would need to take a sample of 60 people (20% of the population) in order to get 20 people from each age group. This could be done by randomly selecting 18 people from the 20-40 age group, 24 people from the 40-60 age group, and 18 people from the 60-80 age group.

Stratified sampling is often used when there are important subgroups within a population that need to be represented in the sample. It can help to ensure that the sample is representative of the population as a whole, and can improve the accuracy of the survey results. However, it can also be more time-consuming and expensive than other sampling methods.

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If Suzy is picking up a cookie from a "cookie-jar", then the probability that Sizy will not pick a "vanilla-cookie" is 0.7.

The "Probability" of Suzy not picking a vanilla cookie can be found by first calculating the total number of cookies in the jar and then subtracting the number of vanilla cookies from it.

Then at last, we divide this number by the total number of cookies in the jar.

The total number of cookies in the jar is : 4 + 3 + 2 + 1 = 10,

The number of vanilla cookies in the jar is = 3.

So, the number of non-vanilla cookies is : 10 - 3 = 7,

The probability that Suzy will not pick a vanilla cookie is : 7/10,

Therefore, the probability that Suzy will not pick a vanilla cookie is 7/10 or 0.7, which is equivalent to a percentage of 70%.

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The given equation is 6 sec^2(x) - 6 = 0. To find all solutions in the interval [0, 2π), we first need to solve for sec^2(x).

1. Isolate sec^2(x) by adding 6 to both sides of the equation:

6 sec^2(x) - 6 + 6 = 0 + 6
6 sec^2(x) = 6

2. Divide both sides of the equation by 6:

sec^2(x) = 1

3. Since sec(x) is the reciprocal of cos(x), we can rewrite the equation as:

1/cos^2(x) = 1

4. Take the reciprocal of both sides:

cos^2(x) = 1

5. Now, find the square root of both sides:

cos(x) = ±1

6. Find the values of x within the interval [0, 2π) for which cos(x) equals 1 or -1:

cos(x) = 1, x = 0, 2π
cos(x) = -1, x = π

Therefore, the solutions to the equation 6 sec^2(x) - 6 = 0 in the interval [0, 2π) are x = 0, π, and 2π. The final answer is: x = 0, π, 2π.

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A simple random sample of 64 kitchen toasters should be taken to determine the mean operational lifetime with a margin of error of 8 hours and a 95% confidence level.

A confidence interval estimates the range within which a population parameter (in this case, the mean operational lifetime) is likely to lie, based on a sample statistic. The margin of error is the maximum amount by which the sample statistic might deviate from the true population value.

The sample size (n) can be calculated using the formula: n = (Z * σ / [tex]E)^2[/tex] where Z is the Z-score corresponding to the desired confidence level, σ is the population standard deviation, and E is the margin of error. The Z-score is a measure of how many standard deviations a data point is from the mean of a distribution. It can be looked up in a Z-score table, or calculated using software, for a specific confidence level. Commonly used confidence levels include 90%, 95%, and 99%.

To calculate the sample size needed for a confidence interval with a margin of error of 8, we need to use the formula:

n = ([tex](z-value)^2[/tex] * σ[tex]^2)[/tex] / ([tex]E^2[/tex])

Where:
- n is the sample size
- z-value is the critical value for the desired confidence level (let's assume a 95% confidence level, so z-value is 1.96)
- σ is the population standard deviation (given as )
- E is the margin of error (given as 8)

Plugging in the values, we get:

n = [tex]((1.96)^2 * ^2) / (8^2)[/tex]
n = [tex](3.8416 * ^2) / 64[/tex]
n = 0.2373 *

Rounding up to the nearest whole number, the sample size needed is:
n = 64

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To find the probability of passing the true/false examination when the number of questions is n, we need to use binomial distribution. We need to plug in the values and calculate the probability of passing for a specific number of questions n

Let X be the number of correct answers the student gets. Since the probability of getting a correct answer is 0.50, we have X ~ Bin(n, 0.50).

To pass the exam, the student must answer at least 70% of the questions correctly. This means that X must be greater than or equal to 0.70n. We can write this as:

P(X >= 0.70n) = 1 - P(X < 0.70n)

Using the binomial distribution formula, we can find the probability of getting less than 0.70n correct answers:

P(X < 0.70n) = ∑(i=0 to 0.70n-1) (n choose i) * 0.50^i * 0.50^(n-i)

We can use a calculator or software to evaluate this sum. For example, if n = 50, we get:

P(X < 0.70n) = P(X < 35) = 0.0738

Therefore, the probability of passing the exam when the number of questions is 50 is:

P(X >= 0.70n) = 1 - P(X < 0.70n) = 1 - 0.0738 = 0.9262

So, the student has a 92.62% chance of passing the exam if there are 50 true/false questions and he answers them by tossing a coin.

To find the probability of passing the true/false examination with a 70% correct answer requirement, we will use the binomial probability formula. The binomial probability formula is:

P(X = k) = C(n, k) * p^k * (1-p)^(n-k)

Where:
- P(X = k) is the probability of getting k correct answers out of n questions
- C(n, k) is the number of combinations of n items taken k at a time
- p is the probability of getting a correct answer (0.50 in this case)
- n is the number of questions
- k is the number of correct answers

Since we need to find the probability of passing when the number of questions is not specified, let's assume there are n questions. To pass the exam, the student must answer at least 70% of the questions correctly. Therefore, k must be greater than or equal to 0.7n.

The probability of passing the exam can be calculated by summing up the probabilities of getting at least 70% correct answers:

P(passing) = sum(P(X = k)) for k = ceil(0.7n) to n

Where ceil() is the ceiling function that rounds up to the nearest integer.

Now we need to plug in the values and calculate the probability of passing for a specific number of questions n. Please provide the number of questions on the examination to get the exact probability of passing.

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The ladder touches the wall at a height of approximately 9.2 feet above the ground.

We can use the Pythagorean theorem to solve this problem. The theorem states that in a right triangle, the square of the length of the hypotenuse (the longest side, which is the ladder in this case) is equal to the sum of the squares of the lengths of the other two sides (the distance from the wall and the height of the ladder on the wall).

Let's call the distance from the wall "x" and the height of the ladder on the wall "h". Then we have:

[tex]x^2 + h^2 = 14^2[/tex]

We also know that the bottom of the ladder is placed 3 feet from the wall, so we have:

x + 3 = 14

Solving for x, we get:

x = 11

Substituting this value into the first equation, we get:

[tex]11^2 + h^2 = 14^2[/tex]

Simplifying and solving for h, we get:

[tex]h = \sqrt{(14^2 - 11^2)}[/tex]

h ≈ 9.2

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All five brothers combined have $20.00 using algebraic equation.

To solve this problem, we first need to find out how much money Peter and his brothers have combined. We know that 30% of the total money is Peter's, so we can use that to find the total amount of money. If $6.00 is 30% of the total, then we can set up an equation:

0.30x = 6.00

To solve for x, we can divide both sides by 0.30:

x = 20.00

This means that the total amount of money that Peter and his brothers have combined is $20.00.

To find out how much each brother contributed, we can divide the total by the number of brothers:

20.00 / 5 = 4.00

So each brother contributed $4.00 to the purchase of the video game.

Therefore, all five brothers combined have $20.00. It is important to remember that in order to find out how much each person contributed, we need to divide the total amount by the number of people involved. In this case, since there were five brothers, we divided the total by 5 to get the individual contribution of $4.00 per brother.

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You must take into account the qualities of the function as well as the kinds of inputs and outputs it takes and generates when determining a function's domain and range limits.

The domain means all the possible values of x and the range means all the possible values of y.

Domain restrictions: If the function returns a fraction, we must rule out any values that might result in the denominator being equal to zero.

Range restrictions: If the function contains a vertical asymptote, the range is constrained so that it excludes any values that are close to the vertical asymptote.

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In case-control studies, the indirect measure of association between the frequency of exposure and frequency of outcome is known as the Odds Ratio (OR). This statistical tool is used to estimate the likelihood of an outcome occurring in an exposed group compared to a non-exposed group. It provides insight into the strength of association between a potential risk factor and a specific outcome or disease.

Odds Ratio is calculated by comparing the odds of exposure in cases (individuals with the outcome) to the odds of exposure in controls (individuals without the outcome). A value of 1 indicates no association between exposure and outcome, while values greater than 1 suggest a positive association, and values less than 1 indicate a negative association or protective effect.

In summary, Odds Ratio is a valuable measure used in case-control studies to understand the relationship between exposure and outcome. It helps researchers identify potential risk factors or protective factors for a specific disease or health condition, allowing for better prevention and intervention strategies.

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The fact that black youths are tried as adults more frequently than other races is a statistic. However, without further information such as sample size and significance level, it is difficult to determine the contribution to the test statistic and the p-value.

A larger sample size could potentially increase the test statistic and decrease the p-value, indicating a stronger relationship between race and being tried as an adult. On the other hand, a smaller sample size could result in a weaker relationship and a higher p-value. It is important to consider all relevant factors when interpreting statistics and making conclusions.
This result contributes to the test statistic, which is a numerical value that helps determine if there is a significant difference between the compared groups.

In this case, the test statistic would be larger, indicating a greater difference between the rates of black youths tried as adults and those of other races. A larger test statistic typically leads to a smaller p-value, which is a measure of the probability of observing such a difference by chance alone. A smaller p-value (usually less than 0.05) suggests that the observed difference is not due to chance and is statistically significant, indicating a possible disparity in the treatment of black youths within the justice system.

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Thus, the rate at which the area of the triangle changes when the height is 18 cm and the base is 9 cm is -45 square centimeters per hour.

To find the rate at which the area of the triangle changes, we need to use the formula for the area of a triangle:

A = (1/2)bh, where A is the area, b is the base, and h is the height.

We are given that the base is shrinking at a rate of 7cm/hr and the height is increasing at a rate of 4cm/hr. This means that the rate of change of the base is -7cm/hr (negative because it is shrinking) and the rate of change of the height is 4cm/hr.

To find the rate at which the area is changing, we need to use the product rule of differentiation.

dA/dt = (1/2)(b dh/dt + h db/dt)

Substituting the given values for b, h, db/dt, and dh/dt, we get:

dA/dt = (1/2)(9*4 + 18*(-7))

dA/dt = (1/2)(36 - 126)

dA/dt = (1/2)(-90)

dA/dt = -45

Therefore, the rate at which the area of the triangle changes when the height is 18 cm and the base is 9 cm is -45 square centimeters per hour.

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Emma spends 1 hour and 3/8 hours on the experiment altogether.

To get the total time Emma spent on the experiment, we have to add the time she spent preparing the experiment and the time she spent doing the experiment.

Now, according to the question,

Time spent preparing the experiment = 3/4 hour.

Time spent doing the experiment = 5/8 hour.

After adding these two fractions, we have:

6/8 + 5/8 = 11/8

So, Emma spent a total of 11/8 hours on the experiment.

Since, 11/8 can be expressed as 1 3/8 by dividing the numerator (11) by the denominator (8) to get the whole number (1) and the remainder (3), which becomes the numerator of the fraction with the same denominator.

Therefore, Emma spent a total of 1 hour and 3/8 hour on the experiment altogether.

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Hi! I'd be pleased to assist you with your permutations, probability, and position-related query. If a set of 24 children—14 boys and 10 girls—were lined up in a random order, what is the likelihood that the child in the 16th spot is a boy?

Step 1: Compute all possible combinations for the 24 children.

There are 24 children, hence there are 24 possible permutations in all! (Factorial, 24).

Step 2: Determine how many permutations there are with a boy in the sixteenth place.

Consider that there are currently 23 seats available for the remaining 13 boys and 10 girls to do this. The remaining children can be set up in 23! (23 factorial) different ways as a result.

Step 3: Use a to divide the permutations.

Step 4: Calculate the probability.
Probability = (Permutations with a boy in the 16th position) / (Total permutations)
Probability = (14 * 23!) / (24!)

Now, to simplify this expression, we can divide both the numerator and the denominator by 23!:
Probability = (14 * 23!) / (24! * 23! / 23!)
Probability = 14 / 24

Step 5: Simplify the probability.
Probability = 7/12

So, the probability that the person in the 16th position is a boy is 7/12.

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To find the probability that the person in the 16th position is a boy, we need to first calculate the total number of possible permutations in which any person can be in the 16th position. This can be calculated using the formula for permutation, which is n!/(n-r)!, where n is the total number of people and r is the number of positions.

So, the total number of permutations for 24 people in random order is 24!/(24-1)! = 24!

Next, we need to find the number of permutations in which a boy is in the 16th position. Since there are 14 boys and 10 girls, we can choose one of the 14 boys for the 16th position and then arrange the remaining 23 people in any order. This can be calculated using the formula for combination, which is nCr = n!/(r!(n-r)!), where n is the total number of items and r is the number of items chosen.

So, the number of permutations with a boy in the 16th position is 14C1 * 23! = 14 * 23!

Therefore, the probability of a boy being in the 16th position is the number of permutations with a boy in the 16th position divided by the total number of permutations, which is:

(14 * 23!)/24! = 14/24 = 7/12

In conclusion, the probability that the person in the 16th position is a boy is 7/12.

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Answer:

Graph as x and y as I put it below

Step-by-step explanation:

x        y

-9      2

-8      1

-7      0

-6      1

-5      2

The distance from point A to segment XZ will be 3.2 units.

We have,

X(-1, -2), Y(0, 1), Z(2, 7) and A(3, 0)

Since segment AY is perpendicular to segment XZ, so we will use points A and Y to find distance between point A to segment XZ.

Using Distance Formula

XZ = √(3-0)² + (0-1)²

XZ = √(3)² + (1)²

XZ = √9 + 1

XZ = √10

XZ = 3.2 unit

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The probability that exactly 8 peas are green, from the random selection would be 22. 56 %.

This is a binomial probability problem as the probability of an exact likelihood from an event needs to be found.

The relevant formula is:

P ( X = k ) = C ( n , k) x p^ k x q ^( n - k)

Solving for the probability, that exactly 8 peas are green gives:

P ( X = 8 ) = C( 12, 8) x ( 3 / 4 ) ^8 x ( 1 / 4 )^4

P ( X = 8 ) = (12! / ( 8 ! ( 12 - 8 )!) ) x ( 3/4 ) ^8 x ( 1 / 4 ) ^4

P ( X = 8 ) = 0. 2256

P ( X = 8 ) = 22. 56 %

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An equation to represent the given scenario is 1/2 +x +1/6 =1 and the part of pizza ate by Jill is 1/3.

Given that, Jack ate 1/2 of the pizza and 1/6 of the pizza was left.

Let the part of pizza ate by Jill be x.

Here, the equation is 1/2 +x +1/6 =1

(1/2 + 1/6) +x=1

(3/6 + 1/6)+x=1

4/6 +x=1

x=1- 2/3

x=1/3

Therefore, an equation to represent the given scenario is 1/2 +x +1/6 =1 and the part of pizza ate by Jill is 1/3.

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The method for including the garage square footage in the total depends on national standards, with some including it in the total and others excluding it.

When including the garage square footage in the total square footage of a home, there are different ways to do it depending on the national standards used.

Generally speaking, there are two main methods:

Including the garage in the total square footage, or excluding the garage from the total square footage.

If the garage is included in the total square footage, then Bob would add the square footage of the garage (600 square feet) to the square footage of the living spaces in the home (e.g., bedrooms, bathrooms, kitchen, living room, etc.).

This would give him the total square footage of the home, including the garage.

This method is commonly used in some areas of the United States.

On the other hand, if the garage is excluded from the total square footage, then Bob would only count the square footage of the living spaces in the home.

This method is commonly used in other areas of the United States, as well as in other countries.

It is important to note that the method used to calculate the total square footage can affect the perceived value of the home, as well as the taxes and insurance premiums associated with it.

Therefore, it is important for Bob to consult with a local real estate professional or appraiser to determine the most appropriate method for his area.

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There is enough proof to indicate that the percentage of newsletter readers who possess a Rolls Royce is less than 69%.

The null hypothesis is that p = 0.69, meaning that 69% of the newsletter readers own a Rolls Royce. The alternative hypothesis is that p < 0.69, meaning that less than 69% of the newsletter readers own a Rolls Royce.

We can use a one-tailed z-test to test the hypothesis. Assuming a sample size of n = 100, if we observe fewer than 69 Rolls Royces in our sample, we can reject the null hypothesis.

Using a z-test, we can calculate the z-score by using the formula:

z = (p' - p) / sqrt(p * (1 - p) / n)

where p' is the sample proportion, p is the hypothesized proportion, and n is the sample size.

At a significance level of 0.10, the critical z-value is -1.28. If the calculated z-score is less than -1.28, we can reject the null hypothesis.

If we conduct a survey of 100 newsletter readers and find that 58 of them own a Rolls Royce, the sample proportion would be p' = 0.58. Calculating the z-score, we get:

z = (0.58 - 0.69) / sqrt(0.69 * 0.31 / 100) = -1.83

Since -1.83 is less than -1.28, we can reject the null hypothesis and conclude that there is sufficient evidence to suggest that fewer than 69% of the newsletter readers own a Rolls Royce.

To know more about null hypothesis, refer here:

https://brainly.com/question/31525353#

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