The equation (x+56)1/2^=x has an extraneous solution.
True or false

The break-even point for bench production at the new plant is 4000 units.

The new production plant of Franco Cooperation will cost SAR 200000 to construct.

The variable costs of production of each bench are SAR 200. The selling price of each bench is SAR 250. We can find out the break-even point of bench production at the new plant by using the break-even formula.

Break-even point (in units) = Fixed costs / Contribution margin per unitWhere,

F = Fixed costs

P = Price per unit

V = Variable cost per unit

Contribution margin per unit = Price per unit - Variable cost per unit

First, let's calculate the contribution margin per unit.

P = Selling price of each bench = SAR 250V = Variable cost per unit = SAR 200

Contribution margin per unit = P - V= SAR 250 - SAR 200= SAR 50

Now, let's calculate the break-even point.

Fixed costs (F) = Cost of constructing the new plant= SAR 200000

Contribution margin per unit = SAR 50

Break-even point (in units) = F / Contribution margin per unit= SAR 200000 / SAR 50= 4000.

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The break-even point for bench production at the new plant can be found by dividing the fixed costs by the contribution margin per unit.

The break-even point for bench production at the new plant is 4000 benches.

To determine the break-even point, we need to calculate the contribution margin per unit. Contribution margin per unit is the amount left over after deducting the variable costs from the selling price. It is also called unit contribution margin. Therefore, the contribution margin per unit = Selling price per unit - Variable cost per unit

= SAR 250 - SAR 200

= SAR 50

Now, we can use the formula to calculate the break-even point:

Break-even point = Fixed costs / Contribution margin per unit

Fixed costs = cost of new production plant

= SAR 200000

Contribution margin per unit = SAR 50

Therefore, Break-even point = SAR 200000 / SAR 50

= 4000 benches

The break-even point for bench production at the new plant is 4000 benches.

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Answer:

answer is 12 miles vause he can travel faster

Step-by-step explanation:

Clients with a QuickBooks Online Plus subscription have the ability to create a total of 400 ungrouped tags and 1000 grouped tags, which can be distributed among up to 40 tag categories.

QuickBooks Online Plus offers users the flexibility to categorize transactions using tags. Tags are a way to organize and track transactions based on specific criteria or categories. There are two types of tags available: ungrouped tags and grouped tags.

With a QuickBooks Online Plus subscription, clients can create a maximum of 400 ungrouped tags. These tags can be assigned to individual transactions to provide additional information or categorization. Clients can create up to 40 tag categories and distribute the 1000 grouped tags among these categories.

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The length of the longest side of the parallelogram is 14.7 cm.

We have,

Let's denote the length of the long sides of the parallelogram as L.

In a parallelogram, the opposite sides are congruent, so both long sides have the same length.

The Law of Cosines states:

c² = a² + b² - 2ab cos(C)

Where:

c is the length of the side opposite the angle C,

a and b are the lengths of the other two sides,

C is the measure of the angle opposite side c.

The short sides of the parallelogram are 12.0 cm, and the acute angle is 65°.

Applying the Law of Cosines to find the length of the long side:

[tex]L^2 = 12^2 + 12^2 - 2 * 12 * 12 * cos(65)\\L^2 = 144 + 144 - 288 * cos(65)\\L^2 = 288 - 288 * cos(65)\\L = \sqrt{(288 - 288 * cos(65))}\\L = 14.7 cm[/tex]

(rounded to the nearest tenth of a centimeter)

Thus,

The length of the longest side of the parallelogram is 14.7 cm.

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Answer:

45/49

decimal form:

0.91836734

Step-by-step explanation:

Y(s) = 6/s * 1/(s * (s - 3)) + 1/s * 5 + 1/(s - 3) * 12

The solution to the initial value problem is :

y(t) = 12e³ᵗ + 3.

We have 3t y'' - 9y' + 18y = 6t e

Taking Laplace transform on both sides, we get

3L(ty'') - 9L(y') + 18L(y) = 6L(te)

Using Laplace transform formulas, we get:

3[s²Y(s) - sy(0) - y'(0)] - 9[sY(s) - y(0)] + 18Y(s) = 6/s²L(e)

⇒ 3s²Y(s) - 3s(5) + 6 - 9sY(s) + 45 + 18Y(s) = 6/s² * 1/sY(s)[3s² - 9s + 18] = 6/s² * 1/s - 3s + 12Y(s) = 6/s * 1/(s * (s - 3)) + 1/s * 5 + 1/(s - 3) * 12

Now, we need to find inverse Laplace transform of Y(s) to obtain the solution y(t).

Let's solve for the first term by Partial Fraction Expansion.

6/s * 1/(s * (s - 3))= A/s + B/(s - 3)6 = A(s - 3) + Bs

Therefore, A = -2 and B = 2y(t) = L⁻¹[Y(s)] = L⁻¹[6/s * 1/(s * (s - 3)) + 1/s * 5 + 1/(s - 3) * 12]= -2L⁻¹[1/s] + 2L⁻¹[1/(s - 3)] + 5L⁻¹[1/s] + 12L⁻¹[1/(s - 3)]= -2 + 2e³ᵗ + 5 + 12e³ᵗ= 12e³ᵗ + 3

Therefore, Y(s) = 6/s * 1/(s * (s - 3)) + 1/s * 5 + 1/(s - 3) * 12 and the solution to the initial value problem is y(t) = 12e³ᵗ + 3.

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Answer: Probability that one of the three components will fail is about 0.045.

Step-by-step explanation:

Answer:

20x + 16

Step-by-step explanation:

Width (w) = 7x - 2

Length (l) = 3x + 10

Perimeter = 2*(l + w)

                 = 2* (3x + 10 + 7x - 2)

                 = 2* (3x + 7x + 10 - 2 )   {Combine like terms}

                 = 2* ( 10x + 8)         {Use distributive property: a(b +c) =(a*b) + (a*c)}

                 = 2*10x + 2*8

                  = 20x + 16

Answer:

I can' see this. Can you show it closer? Please!

Step-by-step explanation:

Answer:

ok maybe itll work with this???

Step-by-step explanation:

file:///C:/Users/apple/Downloads/13f2bddc55b71689635395017a167da1%20(1).pdf

I hope it workksss

Answer:

yes

Step-by-step explanation:

We are to determine if 6 yards is enough t to go round the perimeter of the window

The length is not given, so we have to determine the length from the area

Area of a rectangle = length x breadth

19.25 = 3.5 x length

length = 5.5 feet

Perimeter = 2 x ( length + breadth )

2 x (5.5 + 3.5) = 18 feet

We need to convert the string to foot

1 yard = 3 foot

6 x 3 = 18 foot

the string and the perimeter are equal, so it is enough

Answer:

48

Step-by-step explanation:

The nth term of an AP is expressed as;

Tn = a+(n-1)d

Id 12th term is 32, hence;

T12 = a+11d

32 = a+11d ...1

If the 5th term is 18, then;

T5 = a+4d

18 = a + 4d ....2

Subtract 1 from 2;

32 - 18 = 11d - 4d

14 = 7d

d = 14/7

d = 2

From 1; 32 = a+11d

32 = a+ 11(2)

32 = a + 22

a = 32-22

a = 10

Get the 20th term

T20 = a+19d

T20 = 10 + 19(2)

T20 = 10 + 38

T20 = 48

Hence the 20th term is 48

Answer:

(-2, -4)

Step-by-step explanation:

Answer:

a

Step-by-step explanation:

the 5y and the negative one cancel each other out. add the rest together you end up with 5x=-15. and divide each side by 5. you'll end up with x=-3

Answer:

First answer is 76.50 and the second one is scale factor of 1/2

Step-by-step explanation: Mark brainlist or im deleting my answer :)

Answer:

scale factor = [tex]\frac{3}{2}[/tex]

Step-by-step explanation:

The scale factor is the ratio of corresponding sides, image to original, that is

scale factor = [tex]\frac{WX}{AB}[/tex] = [tex]\frac{12}{8}[/tex] = [tex]\frac{3}{2}[/tex]

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

1 pizza costs $12.75 and feeds 4

To feed 24 students he needs 24 ÷ 4 = 6 pizzas , then

6 cost = 6 × $12.75 = $76.50

Answer:  = -8

Step-by-step explanation: Your welcome!

Answer:

See Explanation

Step-by-step explanation:

I will answer this question with the attached square pyramid

From the attached pyramid, we have:

[tex]Base\ Length = 20m[/tex]

So, the base area is:

[tex]Area = Length * Length[/tex]

[tex]A_1= 20m*20m[/tex]

[tex]A_1= 400m^2[/tex]

The dimension of each of the 4 triangles is:

[tex]Height = 16.4m[/tex]

[tex]Base = 20m[/tex]

So, the area of 4 triangles is:

[tex]Area = 4 * 0.5 * Base * Height[/tex]

[tex]A_2 = 4 * 0.5 * 20m * 16.4m[/tex]

[tex]A_2 = 656m^2[/tex]

So, the surface area is:

[tex]Area = A_1 + A_2[/tex]

[tex]Area = 400m^2 + 656m^2[/tex]

[tex]Area = 1056m^2[/tex]

The statement "If the conclusion of an argument is a tautology, then the counterexample set of that argument must be inconsistent" is true.

Let's understand why?

Explanation:

An argument with a tautology conclusion is an argument that arrives at a conclusion that is always true, regardless of the truth values of the premises. In other words, it is impossible for the premises to be true while the conclusion is false.

This means that any attempt to find a counterexample that disproves the conclusion will always fail, as there is no possible scenario in which the conclusion is false.

The counterexample set of an argument is the set of all possible scenarios in which the premises are true but the conclusion is false. If the conclusion is a tautology, then there is no possible scenario in which the conclusion is false, and thus the counterexample set is empty. An empty counterexample set is equivalent to an inconsistent counterexample set, as it means that there is no consistent scenario in which the conclusion is false.

Therefore, if the conclusion of an argument is a tautology, then the counterexample set of that argument must be inconsistent.

Hence, the statement is true.

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The computed values are:

u + v = (-3, -2)

v + u = (-3, -2)

5u = (10, -15)

2u + 3v = (-11, -3)

2u + 4w = (0, 2, 0)

u - v + 2w = (5, 0, 0)

|v + w| = 7.95

Vector addition is the operation of adding two vectors together to obtain a new vector. It is performed by adding the corresponding components of the vectors. For example, if we have two vectors u = [tex](u_1, u_2, u_3)[/tex] and v = [tex](v_1, v_2, v_3)[/tex], their sum u + v is given by [tex](u_1 + v_1, u_2 + v_2, u_3 + v_3)[/tex].

Scalar multiplication is the operation of multiplying a vector by a scalar (a real number). It is performed by multiplying each component of the vector by the scalar. For example, if we have a vector u = [tex](u_1, u_2, u_3)[/tex] and a scalar k, their product k * u is given by [tex](k * u_1, k * u_2, k * u_3[/tex]).

Both vector addition and scalar multiplication are fundamental operations in linear algebra and are used to manipulate and combine vectors in various applications.

To compute the given expressions, we perform vector addition and scalar multiplication as follows:

u + v =

[tex]= (2, -3) + (-5, 1) \\= (2 - 5, -3 + 1) \\= (-3, -2)[/tex]

v + u =

[tex]=(-5, 1) + (2, -3) \\= (-5 + 2, 1 - 3) \\= (-3, -2)[/tex]

5u =

[tex]= 5 * (2, -3) \\= (5 * 2, 5 * -3)\\ = (10, -15)[/tex]

2u + 3v =

[tex]=2 * (2, -3) + 3 * (-5, 1) \\= (4, -6) + (-15, 3)\\ = (4 - 15, -6 + 3) \\= (-11, -3)[/tex]

2u + 4w =

[tex]= 2 * (2, -3) + 4 * (-1, 2, 3/2) \\= (4, -6) + (-4, 8, 6)\\ = (4 - 4, -6 + 8, -6 + 6)\\ = (0, 2, 0)[/tex]

u - v + 2w =

[tex]= (2, -3) - (-5, 1) + 2 * (-1, 2, 3/2) \\= (2, -3) + (5, -1) + (-2, 4, 3) \\= (2 + 5 - 2, -3 - 1 + 4, 0 - 3 + 3) \\= (5, 0, 0)[/tex]

|v + w| =

[tex]= |(-5, 1) + (-1, 2, 3/2)| \\= |(-5 - 1, 1 + 2, 0 + 3/2)| \\= |(-6, 3, 3/2)| \\= \sqrt{((-6)^2 + 3^2 + (3/2)^2)} \\= \sqrt{(36 + 9 + 9/4)} \\= \sqrt{(63.25)} \\= 7.95[/tex]

Therefore, the computed values are:

u + v = (-3, -2)

v + u = (-3, -2)

5u = (10, -15)

2u + 3v = (-11, -3)

2u + 4w = (0, 2, 0)

u - v + 2w = (5, 0, 0)

|v + w| = 7.95

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The following statement is an example of a null hypothesis: There is no difference between the average GWA of resident students and the average GWA of commuter students, and if there is, it is due to chance.

In statistics, a null hypothesis is a statement that assumes there is no difference between the two variables being tested. The null hypothesis is the statement that the researcher is attempting to disprove in favor of the alternative hypothesis. The null hypothesis can either be rejected or not rejected by the researcher after conducting statistical analysis.

In this case, the null hypothesis states "There is no difference between the average GWA (General Weighted Average) of resident students and the average GWA of commuter students, and if there is, it is due to chance." This means that the null hypothesis assumes that there is no significant distinction in the average GWA between resident students and commuter students. Any observed differences, if they exist, are attributed to random chance rather than a systematic difference between the two groups.

When conducting hypothesis testing, we compare the observed data to the null hypothesis to determine if there is enough evidence to reject the null hypothesis in favor of an alternative hypothesis.

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Answer:

4

Step-by-step explanation:

Line up the numbers at the decimal point and then find the number the same number of spaces away from the decimal point.

12,345.00

00067.89

Answer:

(5,354 + x)

or

536.4*x

Step-by-step explanation:

We know that x = 10.

Now we want to write an expression (in terms of x) for the number 5,364.

This could be really trivial, remember that x = 10.

Then:  (x - 10) = 0

And if we add zero to a number, the result is the same number, then if we add this to 5,364 the number does not change.

5,364 = 5,364 + (x - 10) = 5,364 + x - 10

5,364 = 5,354 + x

So (5,354 + x) is a expression for the number 5,364 in terms of x.

Of course, this is a really simple example, we could do a more complex case if we know that:

x/10 = 1

And the product between any real number and 1 is the same number.

Then:

(5,364)*(x/10) = 5,364

(5,364/10)*x = 5,364

536.4*x = 5,364

So we just found another expression for the number 5,364 in terms of x.

Answer:

10 inches

Step-by-step explanation:

5/6*12

5*2 (since 12/6=2)

10 inches long!

hope it helps you!

Answer:

It would be 10 inches

Step-by-step explanation:

The 5/6 of 12 is 10 since(or you can simply say that we just subtract 2, I don't really know how to explain my work)

In regression models, there are different methods that can be used to evaluate the accuracy of the model and the relationship between the variables. One of the most commonly used methods for evaluating the accuracy of the model is by calculating the R-squared value.

R-squared value represents the proportion of variation in the dependent variable that is explained by the independent variable(s). It ranges from 0 to 1, with a higher value indicating a better fit. To evaluate the accuracy of the model is to use residual plots. Residual plots can be used to identify patterns or trends in the errors or residuals, which can help to identify potential problems with the model and suggest ways to improve it. Additionally, the residuals can be tested for normality and homoscedasticity. Normality can be checked using a normal probability plot, and homoscedasticity can be checked using a scatter plot of residuals versus fitted values.

If the residuals are normally distributed and have a constant variance, then the assumptions of the regression model are met. Another way to evaluate the relationship between the variables is to use correlation analysis. Correlation analysis is a statistical technique that measures the strength and direction of the linear relationship between two variables. The correlation coefficient can range from -1 to +1, with a value of 0 indicating no correlation and a value of -1 or +1 indicating a perfect negative or positive correlation, respectively.

However, correlation analysis only measures the strength and direction of the linear relationship and does not take into account other factors that may affect the relationship, such as outliers or nonlinearities.

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(x + 7) is a factor of f(x) = x^3 - 3x^2 + 2x - 8. By using synthetic division, we find that the remainder is 0.

To determine if (x + 7) is a factor of the polynomial f(x) = x^3 - 3x^2 + 2x - 8, we can use synthetic division.

Performing synthetic division with the divisor (x + 7), we set up the division as follows:

  -7 |   1   -3   2   -8

      |_________________

        1  -10  72   -520

The numbers in the bottom row of the synthetic division represent the coefficients of the quotient polynomial. In this case, the quotient polynomial is 1x^2 - 10x + 72, and the remainder is -520.

Since the remainder is not zero, (x + 7) is not a factor of f(x). If (x + 7) were a factor, the remainder would be zero.

Therefore, (x + 7) is not a factor of f(x) = x^3 - 3x^2 + 2x - 8.

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The least square straight line y = -2x + 3 to fit the data points (0, 3), (2, 1), (3, 1) is found. The minimum square error is 61.

Given data points are (0, 3), (2, 1), (3, 1).

To find the least square straight line, y = mx + b.

The line that fits these points will have the minimum square error.(0,3)      y = mx + b;  3 = 0 + b;  b = 3(2,1)        

y = mx + b;  1 = 2m + b;  b = 1 - 2m(3,1)        

y = mx + b;  1 = 3m + b;  b = 1 - 3m

Substitute the value of b in (2) and (3)1 - 2m = 3 - 3m;  m = -2y = mx + b;  

y = -2x + 3

The least square straight line y = -2x + 3 to fit the data points (0, 3), (2, 1), (3, 1) is found.

Now, we need to compute the minimum square error.

Square error of each point: Point 1 (0, 3):  Square error = (3 - 3)² = 0

Point 2 (2, 1):  Square error = (1 - (-4))² = 25

Point 3 (3, 1):  Square error = (1 - (-5))² = 36

The minimum square error is the sum of the square error of all the points, Minimum square error = 0 + 25 + 36 = 61

Therefore, the least square straight line y = -2x + 3 to fit the data points (0, 3), (2, 1), (3, 1) is found. The minimum square error is 61.

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Answer:

Not similar

Step-by-step explanation: