tell whether (-1,-4) is a solution of -2x-y>6

Through hypothesis testing, it can be explained that Type I error occurs when the allergist determines that the percentage of the population who are allergic to some cheese products is at least 30% when it is actually less than 30%. Similarly, Type II error occurs when the allergist assumes that the percentage of the population who are allergic to certain cheese products is less than 30% when it is actually at least 30%

There are two possible outcomes for hypothesis testing, which leads to two different sorts of conclusion-related errors. This is due to the possibility that the estimated value of the measure and the inference made about the population measure from the samples could diverge.

Let's say an allergist wants to investigate the claim that at least 30% of the population has a cheese allergy.

Let p represent the actual percentage of the population that were allergic to certain cheese products.

The following is how the hypotheses are put forth:

\(H_{0}:p < 0.3\\H_{a}:p\geq 0.3\)

The two types of errors that can be committed by the allergist can be described as below:

(a) Type I error:

When the null hypothesis is assumed to be true when it is actually false, type I error arises.

When an allergist determines that the percentage of the population who are allergic to some cheese products is at least 30% when it is actually less than 30%, type I mistake has been committed.

(b) Type II error:

When the null hypothesis is unsuccessfully rejected even when it is false, type II error has occurred.

When an allergist assumes that the percentage of the population who are allergic to certain cheese products is less than 30% when it is actually at least 30%, type II error has occurred.

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The line best illustrates the growth of a facultative anaerobe incubated aerobically is (b) on the graph.

Cells are the basic unit of life, and they carry out a variety of functions within an organism.

When a catalase-negative cell is exposed to hydrogen peroxide, the hydrogen peroxide will not be broken down into water and oxygen as quickly as it would be in a catalase-positive cell (i.e., a cell that does produce catalase).

This can have important implications for the survival of the cell, particularly under aerobic (i.e., oxygen-rich) conditions.

The figure may contain several lines or curves, each of which represents the change in concentration of hydrogen peroxide over time for a different type of cell.

One of these lines will correspond to the catalase-negative cell. Since this cell does not produce catalase, the rate at which hydrogen peroxide is broken down will be slower than in a catalase-positive cell.

Therefore, the line corresponding to the catalase-negative cell should show a slower decrease in hydrogen peroxide concentration over time than the line corresponding to a catalase-positive cell.

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Answer:

20.8853

Step-by-step explanation:

you can't do that cause these no words in expressions

Answer:

12\sqrt{x^{2}\cdot y^{9}}

Step-by-step explanation:

Both the computers will take 18 minutes to do the job together.

The slower computer sends all the company's email in 45 minutes.

The faster computer completes the same job in 30 minutes.

Let's take minutes t  to complete the task together.

As they complete one job, we get the following equation:

\(\frac{t}{45}\)+\(\frac{t}{30}\)=1

LCM of 45 and 30 is:

45 = 3 x 3 x 5

30 = 2 x 3 x 5

LCM = 2 x 3 x 3 x 5 = 90

Now, solving for t;

⇒\(\frac{2t+3t}{90} = 1\\\frac{5t}{90} =1\\5t=90\\\)

Dividing both sides by 5;

\(\frac{5t}{5}=\frac{90}{5}\)

We get t = 18

Hence, it will take both the computers 18 minutes to do the job together.

A company has two large computers. The slower computer can send all the company's emails in 45 minutes. The faster computer can complete the same job in 30 minutes. If both computers are working together, how long will it take them to do the job?

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Using translation concepts, considering S(2,-1), the x-coordinate of S' is given by 6.

A translation is represented by a change in the function graph, according to operations such as multiplication or sum/subtraction in it's definition.

In this problem, the rule is given by:

(x, y) -> (x+4, y-2).

Considering S(2,-1), 4 is added to the x-coordinate in the translation, hence:

2 + 4 = 6.

The x-coordinate of S' is given by 6.

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Creating a discussion question, evaluating prospective solutions, and brainstorming and evaluating possible solutions are steps in problem-solving.

What is problem-solving?

Problem-solving is the method of examining, analyzing, and then resolving a difficult issue or situation to reach an effective solution.

Problem-solving usually requires identifying and defining a problem, considering alternative solutions, and picking the best option based on certain criteria.

Below are the steps in problem-solving:

Step 1: Define the Problem

Step 2: Identify the Root Cause of the Problem

Step 3: Develop Alternative Solutions

Step 4: Evaluate and Choose Solutions

Step 5: Implement the Chosen Solution

Step 6: Monitor Progress and Follow-up on the Solution.

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Answer:

\(y = 4(2^{x} )\)

Step-by-step explanation:

The general form for an exponential function is \(y = kb^{x}\)

When x = 2,  y = 16    So, \(16 = kb^{2}\)    

and when x = 3,  y = 32    So, \(32 = kb^{3}\)

Divide  \(\frac{32}{16} = \frac{kb^{3} }{kb^{2} }\)

2 = b

Now, \(16 = k2^{2}\)

16 = 4k

k = 4

Therefore, the exponential function is \(y = 4(2^{x} )\)

From the initial information, we can find out how many sweaters a single machine can prepare in a day. After that, we can use this information to find out how many sweaters the remaining machines can prepare in 10 days.

Initially, there are 15 machines working for 6 days to prepare 360 sweaters. So, a single machine can prepare 360/(15*6) = 4 sweaters per day.

If 3 machines break, there are 15 - 3 = 12 machines left.

Therefore, in 10 days, the remaining 12 machines can prepare 12 machines * 4 sweaters/machine/day * 10 days = 480 sweaters.

The remaining machines can prepare 480 sweaters in 10 days.

If 15 machines prepare 360 sweaters in 6 days, than

each machine produces = 360 sweaters / 15 machines

                                         = 24 sweaters in 6 days.

If 3 machines get out, the remaining machines are 15 - 3 = 12 machines.

Since we know that each machine produces 24 sweaters in 6 days.

We can calculate the rate at which they produce sweaters per day:

Rate per machine per day = 24 sweaters / 6 days = 4 sweaters per day.

The total number of sweaters that can be prepared in 10 days by the remaining machines is:

Total sweaters = (Rate per machine per day) * (Number of machines) * (Number of days)

                         = 4 sweaters per day * 12 machines * 10 days

                         = 480 sweaters.

Therefore, the remaining machines can prepare 480 sweaters in 10 days.

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9514 1404 393

Answer:

  B)  12%

Step-by-step explanation:

The total cost of Carl's payment plan is ...

  $125 + 6×72.40 = $560

The percentage change from the original price is ...

  %change = ((new price)/(original price) -1) × 100%

  = ($560/$500 -1) × 100% = (1.12 -1)×100%

  = 12%

The increase from the original cost is 12%.

Answer:

Plug in the x and y values into the equation

Step-by-step explanation:

The equation for a cosecant function with vertical asymptotes found at x = nπ such that n is an integer is `y = csc(x - nπ)`.

The cosecant function is the reciprocal of the sine function. The cosecant of a particular angle in a right triangle is the ratio of the hypotenuse to the side opposite the angle.

The cosecant function is written as follows: `csc(x) = 1/sin(x)`

Asymptotes are straight lines or curves that a graph approaches but never touches.

As a result, the graph of a curve gets closer and closer to the asymptote as it moves further away from the origin, but it never touches it.

For a function to have vertical asymptotes at `x = nπ`, the denominator of the function should equal zero. In the cosecant function, the denominator is equal to `sin(x - nπ)`.

Therefore, the vertical asymptotes are where `sin(x - nπ) = 0`.

The general form of the cosecant function is `y = A csc(B(x - C)) + D`.

The constants A, B, C, and D are used to alter the form of the graph of the cosecant function.

They can be used to alter the amplitude, period, phase shift, and vertical displacement of the graph, respectively.

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Using unit conversion, it is obtained that it would take approximately 3,028,328 drops to fill a 40-gallon rain barrel.

What is unit conversion?

Unit conversion is a process with multiple steps that involves multiplication or division by a numerical factor or, particularly a conversion factor. The process may also require selection of the correct number of significant digits, and rounding.

Use the method of unit conversion.

First, we need to convert 40 gallons to milliliters, since we know the conversion rate of drops to milliliters.

We can use the following conversion factors -

1 gallon = 3,785.41 milliliters (exact)

1 gallon = 3,785,410 drops (since 20 drops = 1 milliliter)

So, 40 gallons is -

40 × 3,785.41 = 151,416.4 milliliters

To find how many drops it would take to fill this amount of space, use operation of multiplication.

Multiply the milliliters by the conversion factor -

151,416.4 × 20 = 3,028,328 drops

Therefore, the value is obtained as 3,028,328 drops.

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Answer: x<-3

Step-by-step explanation: first divide both sides of the equation by -6 so then on one side you’d have x>-3 but since you divided by a negative you filp the sign to have x<-3

(a) To calculate the p-value for the given scenario, we need to find the probability of obtaining a test statistic as extreme as the observed value (z = 1.4) under the null hypothesis.

Since the alternative hypothesis is one-sided (µ > 25), we need to calculate the probability of observing a z-value greater than 1.4.

Using a standard normal distribution table or a calculator, we find that the cumulative probability for z = 1.4 is approximately 0.9192.

Therefore, the p-value for this scenario is 1 - 0.9192 = 0.0808.

(b) With a significance level of 0.10, we compare the p-value (0.0808) to the significance level. Since the p-value is greater than the significance level, we fail to reject the null hypothesis (Do Not Reject H0).

(a) To calculate the p-value for the given scenario, we need to find the probability of obtaining a test statistic as extreme as the observed value (t = -2.120) under the null hypothesis.

Since the alternative hypothesis is one-sided (µ < 20), we need to calculate the probability of observing a t-value less than -2.120.

Using a t-distribution table or a calculator, we find that the cumulative probability for t = -2.120 with 10 degrees of freedom is approximately 0.025.

Therefore, the p-value for this scenario is 0.025.

(b) With a significance level of 0.025, we compare the p-value (0.025) to the significance level. Since the p-value is less than the significance level, we reject the null hypothesis (Reject H0).

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a) There is not enough information  b) We can conclude that W≻R and W≻G.  c) The probabilities p(G), p(W), and p(R) are the probabilities of getting each drink in the lottery L.  d) The decision would be whether to drink Whisky or Gin.

(a) There is not enough information to determine whether Cohen prefers a glass of Gin to a glass of Whisky because the lotteries L1 and L2 are indifferent. This means that Cohen is equally likely to prefer one lottery to the other. In other words, his utility for Gin is equal to his utility for Whisky.

(b) If we are also told that Cohen prefers Rum to Gin, R≻G, then we can say that he prefers L3 to L1 and L2. This is because L3 has a higher probability of giving him his most preferred drink, Rum, than either L1 or L2. Therefore, we can conclude that W≻R and W≻G.

If G≻R, then we would have L1≻L3 and L2≻L3. This is because L1 and L2 have a higher probability of giving him his most preferred drink, Gin, than L3. However, we know that L3≻L1 and L3≻L2, so this is a contradiction. Therefore, G≻R cannot be true.

(c) A VNM utility function is a function that represents a person's preferences over lotteries. It takes as input a lottery and outputs a real number that represents the person's utility for that lottery. In the case where W≻G, a VNM utility function for Cohen would be of the form:

u(L) = w * p(G) + g * p(W) + r * p(R)

where w is Cohen's utility for Whisky, g is his utility for Gin, and r is his utility for Rum. The probabilities p(G), p(W), and p(R) are the probabilities of getting each drink in the lottery L.

(d) A VNM utility function cannot be used to represent Cohen's preferences because his preferences are not stable. In other words, his preferences change depending on his mood. When he is happy, he prefers Whisky to Gin. But when he is depressed, he prefers Gin to Whisky. This means that his utility for Gin and Whisky cannot be represented by a single number.

To model Cohen's preferences, we would need to use a more complex model that takes into account his mood. One possible model would be a Markov decision process. A Markov decision process is a model that describes how a person's preferences change over time. It takes as input the person's current mood and outputs a decision about what to do.

In the case of Cohen, the decision would be whether to drink Whisky or Gin. The person's mood would be represented by a state variable. The state variable would take on one of two values: happy or depressed. The transition probabilities would describe how likely it is for the person's mood to change from one state to the other. The reward function would describe how much utility the person gets from drinking each drink.

This model would allow us to capture the fact that Cohen's preferences change depending on his mood. It would also allow us to make predictions about how he would behave in different situations.

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Answer:

6 units

Step-by-step explanation:
To find out how far apart two points are on a graph, we must count the spaces that separate them along the x and y axis

Counting on the x-axis, Point A is 2 units to the left of Point B

On the y-axis, Point A is 4 units below Point B.

Add the two to get the total units

Based on the information given and the figure provided, we can determine the following:

- Since the support at B is a roller, it can only provide a vertical reaction force. Therefore, the horizontal component of the reaction force at B is zero. This means that the shear force at point C is equal to the applied load F = 12 kips.
- To determine the internal normal force at point C, we need to draw a free body diagram of the section of the beam to the left or to the right of point C (it doesn't matter which side we choose). Let's assume we choose the left side, as shown in Figure 2.

(Figure 2)

- The internal normal force N is perpendicular to the cross-section of the beam, and it points away from the section we are considering. Since there are no external vertical forces acting on this section, the internal normal force must be equal in magnitude and opposite in direction to the sum of the vertical components of the applied load and the reaction force at B. Therefore, we have:

N = - (F + VB)

where VB is the vertical reaction force at B. To find VB, we can use the equation of equilibrium of forces in the vertical direction:

VB = F

since there are no other vertical forces acting on the beam. Substituting this into the equation above, we get:

N = - 2F = - 24 kips

Therefore, the internal normal force at point C is equal to -24 kips, and it points away from the section to the left of point C.
- To determine the bending moment in the beam at point C, we need to use the equation of equilibrium of moments about point C:

M = 0 = - (F)(8 ft) + (VB)(6 ft) + (N)(0 ft)

where the signs of the terms are based on the right-hand rule (clockwise moments are negative, counterclockwise moments are positive). Solving for M, we get:

M = - 48 kip-ft

Therefore, the bending moment in the beam at point C is equal to -48 kip-ft, which means that the beam is experiencing a clockwise bending moment at this location.
To determine the internal normal force, shear force, and bending moment at point C, we need to first analyze the beam with the given load and support conditions. Since we don't have a visual representation of the problem, I will assume a simply supported beam with the roller support at B, and point C is located just to the right of the F = 12 kip load.

1. Internal normal force at point C:
In a simply supported beam, the internal normal force is typically zero, as there are no axial loads acting on the beam. Therefore, the internal normal force at point C is 0 kip.

2. Shear force at point C:
To find the shear force at point C, we need to calculate the reactions at the supports. Let's assume the reaction at support A is R_A and at support B is R_B. Since the beam is in equilibrium, the sum of the vertical forces must be equal to zero:
R_A + R_B = 12 kip
Now, considering the moment equilibrium around point A, we get:
R_B * L = 12 kip * (L/2)
R_B = 6 kip
Since R_A + R_B = 12 kip, we find R_A = 6 kip as well.

The shear force at point C, just to the right of the 12 kip load, is equal to the reaction at support A, as there are no other loads between point A and point C:
Shear force at point C = R_A = 6 kip

3. Bending moment at point C:
To determine the bending moment at point C, we can use the moment equilibrium equation considering the section just to the right of point C:
M_C = R_A * x - F * (x - a), where x is the distance from support A to point C, and a is the distance from support A to the 12 kip load.

Since point C is just to the right of the 12-kip load, x = a. Therefore, the bending moment equation simplifies to:
M_C = R_A * x - F * 0
M_C = 6 kip * x

To find the exact value of the bending moment at point C, we need the value of x.

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To make a decision for the hypothesis test, we need to analyze the ANOVA summary table for the test of significance of the overall regression model.

From the table, we can see that the regression sum of squares (SS) is given as 20, and the residual sum of squares (SS) is given as 400. We also know that the total sum of squares (SS) is the sum of the regression SS and the residual SS.

Since the degrees of freedom (df) for the regression model is equal to the number of predictor variables (k) minus 1 (k - 1), and the df for the residual is the total sample size (n) minus the number of predictor variables (k), we can calculate the df for the regression and the residual.

Given that the sample size (n) is 45 and the number of predictor variables (k) is 4, we can calculate:

df for regression = k - 1 = 4 - 1 = 3

df for residual = n - k = 45 - 4 = 41

Next, we need to calculate the mean square (MS) for the regression and the residual by dividing the SS by their respective degrees of freedom.

MS for regression = SS for regression / df for regression = 20 / 3

MS for residual = SS for residual / df for residual = 400 / 41

Finally, we can calculate the F-statistic by dividing the MS for regression by the MS for residual.

F = (MS for regression) / (MS for residual)

Now, we can compare the calculated F-statistic to the critical F-value at the given significance level (α = 0.05). If the calculated F-statistic is greater than the critical F-value, we reject the null hypothesis. Otherwise, we fail to reject the null hypothesis.

Without the information of the critical F-value or the calculated F-statistic, we cannot make a definitive decision or final conclusion for the hypothesis test. Please provide the necessary values, and I will be able to help you with the decision and conclusion.

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The probability of x = 19 is 3⁄50. This can be expressed as a decimal by dividing the numerator and denominator by 50, resulting in 0.06. This means that the probability of x = 19 is 6%.

The sampling distribution is a statistical tool used to estimate the probability of a particular outcome occurring in a sample. It is calculated by dividing the number of observations that meet a certain criteria by the total number of observations. In this case, the sampling distribution is represented as x -20 -9 -3 11 19 p(x) 1/25 2⁄25 1⁄100 3⁄50 ___.To calculate the probability of a particular outcome (x = 19 in this case), the formula p(x) = f(x)/N is used, where f(x) is the number of observations that meet the criteria (in this case x = 19) and N is the total number of observations .Therefore, the probability of x = 19 is 3⁄50. This can be expressed as a decimal by dividing the numerator and denominator by 50, resulting in 0.06. This means that the probability of x = 19 is 6%.

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The determinant of the matrix A using elimination and cofactor expansion is 1717.

To find the determinant of the matrix A using elimination and cofactor expansion, we can use the following steps:

Matrix A:

| 1 23 2 |

| 1 -32 1 |

| 21 25 3 |

Step 1: Apply row operations to the matrix to simplify it:

R2 = R2 - R1:

| 1 23 2 |

| 0 -55 -1 |

| 21 25 3 |

R3 = R3 - 21R1:

| 1 23 2 |

| 0 -55 -1 |

| 0 -428 -39 |

Step 2: Expand the determinant using cofactor expansion along the first row:

det(A) = 1 * cofactor(A, 1, 1) + 23 * cofactor(A, 1, 2) + 2 * cofactor(A, 1, 3)

Step 3: Calculate the cofactors of each element:

cofactor(A, 1, 1) = (-1)^(1+1) * det(minor(A, 1, 1)) = det(minor(A, 1, 1))

cofactor(A, 1, 2) = (-1)^(1+2) * det(minor(A, 1, 2)) = -det(minor(A, 1, 2))

cofactor(A, 1, 3) = (-1)^(1+3) * det(minor(A, 1, 3)) = det(minor(A, 1, 3))

Step 4: Calculate the minors of each element:

minor(A, 1, 1) = | -55 -1 |

| -428 -39 |

minor(A, 1, 2) = | 0 -1 |

| 0 -39 |

minor(A, 1, 3) = | 0 -55 |

| 0 -428 |

Step 5: Calculate the determinants of the minors:

det(minor(A, 1, 1)) = (-55 * (-39)) - (-1 * (-428)) = 2145 - 428 = 1717

det(minor(A, 1, 2)) = 0 * (-39) - (-1 * 0) = 0

det(minor(A, 1, 3)) = 0 * (-428) - (-55 * 0) = 0

Step 6: Substitute the determinant values into the expansion:

det(A) = 1 * 1717 + 23 * 0 + 2 * 0

det(A) = 1717

Therefore, the determinant of the matrix A using elimination and cofactor expansion is 1717.

To find the inverse of the matrix A using the adjoint matrix, we can use the following steps:

Matrix A:

| 1 2 3 |

| 3 0 1 |

| 2 1 0 |

Step 1: Calculate the determinant of matrix A using any method (in this case, we already found it as 1717).

Step 2: Calculate the adjoint matrix of A, which is the transpose of the matrix of cofactors.

Adjoint(A) = | cofactor(A, 1, 1) cofactor(A, 2, 1) cofactor(A, 3, 1) |

| cofactor(A, 1, 2) cofactor

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The solution to the given differential equation is y = (3(x + ln|x| + C₂ - C₁))^(1/3), where C₁ and C₂ are arbitrary constants.

To solve the differential equation xy²y = x + 1, we can use the method of separation of variables.

First, we rearrange the equation to separate the variables: y²dy = (x + 1)/(x) dx

Next, we integrate both sides of the equation with respect to their respective variables: ∫ y² dy = ∫ (x + 1)/(x) dx

For the left-hand side, we have: ∫ y² dy = (1/3) y³ + C₁

For the right-hand side, we have: ∫ (x + 1)/(x) dx = ∫ (1 + 1/x) dx = x + ln|x| + C₂

Combining the two sides, we have: (1/3) y³ + C₁ = x + ln|x| + C₂

Rearranging the equation, we get: y³ = 3(x + ln|x| + C₂ - C₁)

Finally, we can find the solution for y by taking the cube root of both sides: y = (3(x + ln|x| + C₂ - C₁))^(1/3)

Therefore, the solution to the given differential equation is y = (3(x + ln|x| + C₂ - C₁))^(1/3), where C₁ and C₂ are arbitrary constants.

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To find ƒ[A], we need to determine the image of the set A under the function ƒ.

Given A = {3k : k ∈ N}, we can substitute each element of A into the function ƒ(n) = {k ∈ N: knɅk is odd} to find the corresponding images.

Let's evaluate ƒ[A]:

ƒ[A] = {ƒ(n) : n ∈ A}

For n = 3k (where k ∈ N), we have:

ƒ(3k) = {m ∈ N : mɅ(3k) is odd}

To find the values of m that satisfy the condition, we need to consider the parity of 3k and m. Since 3k is always odd, m must also be odd for mɅ(3k) to be odd.

Therefore, ƒ(3k) = {m ∈ N : m is odd} = 2N (the set of all even numbers).

Hence, ƒ[A] = 2N (the set of all even numbers).

Now let's find ƒ^(-1)[ƒ[A]], which represents the preimage of ƒ[A] under the function ƒ.

ƒ^(-1)[ƒ[A]] = {n ∈ N : ƒ(n) ∈ ƒ[A]}

Since ƒ[A] = 2N, which consists of all even numbers, the preimage of ƒ[A] under ƒ will be the set of all natural numbers.

Therefore, ƒ^(-1)[ƒ[A]] = N (the set of all natural numbers).

In summary:

ƒ[A] = 2N (the set of all even numbers)

ƒ^(-1)[ƒ[A]] = N (the set of all natural numbers)

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Answer:

The variable X represents:

X = the number of books

The real world situation can be:

1.) Jennie reads books. She reads each books for

2.) Jennie wants to read book at least 5 books today.

Write an inequality that models the minimum number of books that Jennie needs to read.

The inequality for this situation is:

\(2x \: \geqslant \: 5\)

Answer:

x=42

y=12

Step-by-step explanation:

Answer:

\(278.2 \: ft\)

Step-by-step explanation:

Descended feet:

\(94 \: s \times 3.3 \: \frac{ft}{s} \: = 310.2 \: ft \)

Ascended feet:

\(32 \: s \times 1 \: \frac{ft}{s} = 32 \: ft\)

Total elevation:

\((310.2 - 32) \: ft = 278.2 \: ft\)

Step-by-step explanation:

2.) -20× + 24

maaf kalo salah

Answer:

2nd option

Step-by-step explanation:

tan x = \(\frac{opposite}{adjacent}\) = \(\frac{MO}{MN}\) = \(\frac{8}{15}\)

\(▪▪▪▪▪▪▪▪▪▪▪▪▪  {\huge\mathfrak{Answer}}▪▪▪▪▪▪▪▪▪▪▪▪▪▪\)

Using Trigonometry,

Answer:

red

Step-by-step explanation:

34%

1/5

3/10

convert them all to x/100 fractions:

34/100

20/100

30/100

this means that red has the most amount because it has the largest fraction

Answer:

the answer is red 34% I'm pretty sure