given that x square + 5 x + c is a perfect square find the value of C ​

The cost of goods completed and transferred out, given 16,000 units completed and 1,600 units in ending inventory, is $104,000. This is calculated by multiplying the number of completed units by the weighted-average cost per equivalent unit for direct materials and conversion.

In this scenario, 16,000 units are completed and 1,600 units are in ending inventory. The weighted-average cost per equivalent unit for direct materials is $3.00, and for conversion, it is $3.50.

To calculate the cost of goods completed and transferred out, we can use the following formula:

Cost of goods completed and transferred out = (Number of completed units * Weighted-average cost per equivalent unit for direct materials) + (Number of completed units * Weighted-average cost per equivalent unit for conversion)

Substituting the given values:

Cost of goods completed and transferred out = (16,000 * $3.00) + (16,000 * $3.50)

                                            = $48,000 + $56,000

                                            = $104,000

Therefore, the cost of goods completed and transferred out is $104,000.

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To compute the future worth of the given cash flows, we need to multiply each cash flow by its corresponding discount factor and sum them up. The discount factor can be calculated using the formula:

Discount Factor = 1 / (1 + i)^n

Where:

i is the interest rate (12% or 0.12 in decimal form)

n is the number of periods

Let's calculate the future worth step by step:

Cash Flow: 5x

Discount Factor = 1 / (1 + 0.12)^1 = 1 / 1.12 = 0.8929 (rounded to 4 decimal places)

Future Worth of 5x = 5x * 0.8929 = 4.4645x

Cash Flow: 4x

Discount Factor = 1 / (1 + 0.12)^2 = 1 / 1.2544 = 0.7972 (rounded to 4 decimal places)

Future Worth of 4x = 4x * 0.7972 = 3.1888x

Cash Flow: 3x

Discount Factor = 1 / (1 + 0.12)^3 = 1 / 1.4049 = 0.7118 (rounded to 4 decimal places)

Future Worth of 3x = 3x * 0.7118 = 2.1354x

Cash Flow: 2x

Discount Factor = 1 / (1 + 0.12)^4 = 1 / 1.5735 = 0.6355 (rounded to 4 decimal places)

Future Worth of 2x = 2x * 0.6355 = 1.2710x

Now, let's sum up all the future worth values:

Future Worth = 4.4645x + 3.1888x + 2.1354x + 1.2710x

= 11.0597x

Therefore, the future worth of the given cash flows is 11.0597x.

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Answer:

(3a-7)²

Step-by-step explanation:

Sum=-42

Prodect=441

9a²-21a-21a+49

3a(3a-7)-7(3a-7)

(3a-7)(3a-7)

=(3a-7)²

Answer:

-121 + 7

Step-by-step explanation:

Do some rearanging...

4y^2 - 26 - 8y + 33 - 14y^2 + 67y

4y^2 -8y - 14y^2 + 67y - 26 + 33

= 16y - 8y - 196y + 67y - 26 + 33

= -121y + 7

I did it mentally so I apologize for any mistakes.

Jody has created a systematic sampling error by forgetting to include a segment of the population that is relevant to the study.

Systematic sampling is a sampling method where every nth member of a population is selected to be part of the sample. In Jody's case, she forgot to include a specific segment of the population, which means that the sampling process was not systematic. This error can introduce bias and affect the representativeness of the sample.

No calculations are required for this type of error.

Jody's mistake in not including a relevant segment of the population has resulted in a systematic sampling error. To ensure the accuracy and validity of the study, it is important to rectify this error by including the missing segment or adjusting the sampling method accordingly.

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Answer: -x

Step-by-step explanation:

2x−3+(2)(2)−3x−((3)(2)−5)

Distribute the Negative Sign:

=2x−3+(2)(2)−3x+−1((3)(2)−5)

=2x+−3+(2)(2)+−3x+−1(3)(2)+(−1)(−5)

=2x+−3+(2)(2)+−3x+−6+5

=2x+−3+4+−3x+−6+5

Combine Like Terms:

=2x+−3+4+−3x+−6+5

=(2x+−3x)+(−3+4+−6+5)

Answer:

1/25 cm³

Step-by-step explanation:

Formula for calculating rectangular prism is expressed as:

V = Base Area × Height

Given

Base Length = 2/5 cm

Base width = 1/5 cm

Height of the prism = 1/2 cm

Volume of the rectangular prism = Area of the rectangular base × Height of prism

Volume = (length×width)×(Height of the prism)

Volume = (2/5×1/5)×1/2

Volume of the prism = 2/25×1/2

Volume of the rectangular prism = 2/50 cm³ = 1/25 cm³

The table that represents a linear relationship that is also proportional is :  x: -4, 0, 4

   y: 0, 1, 2

Option D is correct.

A linear relationship is described as being proportional if the ratio of the dependent variable  toy the independent variable x remains constant.

We analyze each table:

Table A:

x: -3, 0, 3

y: 2, 3, 4

We see that the ratio of y to x is not constant as it  changes from 2/(-3) = -2/3, to 3/0 (undefined), to 4/3.

Table B

x: -2, -1, 0

y: 4, 2, 0

We see that the  ratio of y to x is not constant as it changes from 4/(-2) = -2, to 2/(-1) = -2, to 0/0 (undefined).

Table C.

x: 0, 1, 2

y: -2, 1, 4

The ratio of y to x is not constant and changes from -2/0 (undefined), to 1/1 = 1, to 4/2 = 2.

Table D.

x: -4, 0, 4

y: 0, 1, 2

The ratio of y to x is constant and is always 0/(-4) = 0, 1/0 (undefined), 2/4 = 1/2.

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Answer:

[tex]vt = \sqrt{ {20}^{2} + {17}^{2} } \\ = \sqrt{400 + 289 } \\ = \sqrt{689} \\ vw = \sqrt{ {vt}^{2} + {tw}^{2} } \\ = \sqrt{689 + {20}^{2} } \\ = \sqrt{1089} = 33 \\ [/tex]

Answer:

E

Step-by-step explanation:

EA SPORTS> ITS IN THE gAME!!!

Answer:

the difference in height is 1435.5 feet

Answer:

The difference in heights is 1435.5 feet.

Step-by-step explanation:

If the Willis tower is 100x as tall as Leroy's house, its height would be 14.5 times 100, which is 14500. 14500-14.5 is 1435.5, which is the difference in heights.

Answer:

21 miles

Step-by-step explanation:

At this rate, the car will travel 63  : 3 = 21 miles in 1 hour

Answer:

21 miles

Step-by-step explanation:

If the car had already traveled 63 miles in 3 hours then you would divide 63/3 and you would get 21 miles.

Answer: 275 gallons.

Step-by-step explanation:

Answer:

B

Step-by-step explanation:

Make a commone demonatore my multiplying.

Answer:

x=8

Step-by-step explanation:

Answer:

x=8

Step-by-step explanation:

30-3x=3x-24+6

-3x-3x= -24+6-30

-6x= -18-30

-6x= -48

x= -48/-6

x=8

Answer:

C

Step-by-step explanation:

13.50x+3.25 40

Answer:

n = 4/7

Step-by-step explanation:

Step 1: Write equation

8 = 14n

Step 2: Divide both sides by 14

n = 8/14

Step 3: Simplify

n = 4/7

Answer:

y = -3 +7/5 x

Step-by-step explanation:

-7x  = -15 - 5y

-7x + 5y = -15 -5y -5y

-7x +5y = -15

-7x +7x +5y = -15 + 7x

5y = -15 + 7x

5y/5 = -15/5 +7/5x

y = -3 +7/5 x

Answer:

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

Step-by-step explanation:

The equation of a line in slope- intercept form is

y = mx + c ( m is the slope and c the y- intercept )

y = - 2x + 2 ← is in slope- intercept form

with slope m = - 2

Given a line with slope m then the slope of a line perpendicular to it is

[tex]m_{perpendicular}[/tex] = - [tex]\frac{1}{m}[/tex] = - [tex]\frac{1}{-2}[/tex] = [tex]\frac{1}{2}[/tex]

The line crosses the y- axis at (0, 1) ⇒ c = 1

y = [tex]\frac{1}{2}[/tex] x + 1 ← equation of line

Answer:

The circumference of a circle can be found by multiplying pi ( π = 3.14 ) by the diameter of the circle. If a circle has a diameter of 4, its circumference is 3.14*4=12.56. If you know the radius, the diameter is twice as large.

Step-by-step explanation:

^^^

hoping it was helpful

To solve the given linear programming problem graphically, we need to plot the feasible region determined by the intersection of the three constraint lines and then identify the corner points within this region to determine the optimal solution. The constraints of the problem are:

1) 1X ≤ 9

2) 2X + 3Y ≤ 28

3) X + Y ≤ 12

4) X, Y ≥ 0 We can start by graphing the equality lines for each constraint. The first constraint, 1X ≤ 9, represents a vertical line passing through the point (9,0). The second constraint, 2X + 3Y ≤ 28, represents a line with an intercept of 9.33 on the Y-axis and a slope of -2/3. The third constraint, X + Y ≤ 12, represents a line with an intercept of 12 on the Y-axis and a slope of -1. After plotting these lines, we can shade the feasible region by considering the inequalities and their directions. The feasible region will be the area where all the constraints are satisfied. Finally, we can identify the corner points within the feasible region and evaluate the objective function 5X + 7Y at each corner point.

The corner point with the highest objective function value will be the optimal solution. Without a visual representation of the graph, it is not possible to provide the exact values of (X, Y) for the optimal solution. However, by following the steps described above, you can determine the optimal solution graphically by identifying the corner point with the highest value of 5X + 7Y.

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Answer:

y = 3x - 6

y + 3 = 3(x - 1)

Step-by-step explanation:

Slope-Intercept Form: y = mx + b

Point-Slope Form: y - y₁ = m(x - x₁)

Slope Formula: [tex]m=\frac{y_2-y_1}{x_2-x_1}[/tex]

Step 1: Find slope m

[tex]m=\frac{15-(-3)}{7-1}[/tex]

[tex]m=\frac{15+3}{6}[/tex]

[tex]m=\frac{18}{6}[/tex]

[tex]m=3[/tex]

y = 3x + b

Step 2: Find y-intercept b

-3 = 3(1) + b

-3 = 3 + b

b = -6

Step 3: Write linear equation

y = 3x - 6

or

y + 3 = 3(x - 1)

To complete the Material Requirements Planning (MRP) table for product A, we need to calculate the Net Requirements and Planned Order Releases based on the given information. Here's the completed MRP table:

Lot Volume = multiples of 50

Delivery Time = 2 weeks

Starting Inventory: 30

| Period | Gross Need | Scheduled Receipt | Projected Inventory | Net Requirements |

| 1 | 25 | | 30 | 0 |

| 2 | 30 | | | |

| 3 | 56 | | | |

| 4 | 25 | | | |

| 5 | 100 | | | |

| 6 | 40 | | | |

| 7 | 30 | | | |

To calculate the Scheduled Receipt and Projected Inventory, we need to consider the lot volume and delivery time. Assuming a lot volume of 50 and a delivery time of 2 weeks, the calculations are as follows:

Period 1:

Scheduled Receipt = 0 (No orders scheduled to arrive in this period)

Projected Inventory = Starting Inventory + Scheduled Receipt - Gross Need = 30 + 0 - 25 = 5

Period 2:

Scheduled Receipt = 50 (Order placed to fulfill the gross need of 30)

Projected Inventory = Previous Projected Inventory + Scheduled Receipt - Gross Need = 5 + 50 - 30 = 25

Period 3:

Scheduled Receipt = 50 (Order placed to fulfill the gross need of 56)

Projected Inventory = Previous Projected Inventory + Scheduled Receipt - Gross Need = 25 + 50 - 56 = 19

Period 4:

Scheduled Receipt = 0 (No orders scheduled to arrive in this period)

Projected Inventory = Previous Projected Inventory + Scheduled Receipt - Gross Need = 19 + 0 - 25 = -6

Period 5:

Scheduled Receipt = 100 (Order placed to fulfill the gross need of 100)

Projected Inventory = Previous Projected Inventory + Scheduled Receipt - Gross Need = -6 + 100 - 100 = -6

Period 6:

Scheduled Receipt = 50 (Order placed to fulfill the gross need of 40)

Projected Inventory = Previous Projected Inventory + Scheduled Receipt - Gross Need = -6 + 50 - 40 = 4

Period 7:

Scheduled Receipt = 0 (No orders scheduled to arrive in this period)

Projected Inventory = Previous Projected Inventory + Scheduled Receipt - Gross Need = 4 + 0 - 30 = -26

The Net Requirements are calculated as the difference between the Gross Need and the Projected Inventory. If the Net Requirements are positive, it indicates the quantity needed to fulfill the demand. If the Net Requirements are negative, it means there is excess inventory.

Please note that this MRP table assumes no changes in demand, lead time, or lot sizing throughout the periods.

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Answer:

y = 24

Step-by-step explanation:

We can dilate the other sides to figure out what the scale factor is. Once we know the scale factor we can find y.

Divide 12 by 4.

12 / 4 = 3

The scale factor is 3.

Now we must multiply 8 by 3.

8 * 3 = 24.

y = 24.

Answer:

-2/2

Step-by-step explanation:

change in y is -2, change of x is 2

The statement provided is false. The assumption that the error term is mean independent from the regressors x1, x2, and x3, also known as exogeneity, is a crucial assumption for Ordinary Least Squares (OLS) regression. However, this assumption alone does not guarantee that the OLS estimator β is unbiased or that each estimated parameter measures causal effects.

Unbiasedness of the OLS estimator relies on several assumptions, including exogeneity, but it also requires other conditions to hold. One important condition is that the error term has zero conditional mean given the regressors. This condition, known as the conditional expectation of errors being zero (E[ε|X] = 0), ensures that there is no systematic bias in the estimation.

Causal interpretation of the estimated parameters requires additional assumptions beyond exogeneity. In order to establish causal effects, researchers often rely on a combination of study design, theory, and additional assumptions such as the absence of omitted variable bias, correct specification of the functional form, and correct model identification.

Therefore, while exogeneity is an important assumption for OLS regression, it alone does not guarantee unbiasedness of the estimator or the ability to interpret estimated parameters as causal effects. Other assumptions and considerations are necessary to make such claims.

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5) The correct option is C, the equation willl be y = 3n + 7

6) 4z +x = 9x

7) 3y + 2x = 14x

8) 7x = 2y + 3x

A general linear equation can be written as:

y = ax + b

If we know two points in the line (x₁, y₁) and (x₂, y₂), then the slope of the line is:

a = (y₂ - y₁)/(x₂ - x₁)

Here we can use the first two points in the graph (1,10) and (3, 16)

Then the slope is:

a = (16 - 10)/(3 - 1) = 6/3 = 3

So the line is:

y = 3x + b

And we know it passes through (1, 10), then:

10 = 3*1 + b

10 - 3 = b

7 = b

The line is :

y = 3x + 10

So the correct option is c, y = 3n + 7 (the variable is n instead of x).

For the other 3 ones.

Let's define the variables:

x = little circle.

y = bag

z = cylinder.

Then because there is equilibrium in each image, we just need to count how many of each are in each side, and put them in the correspondent sides of an equation, we will get:

6) 4z +x = 9x

7) 3y + 2x = 14x

8) 7x = 2y + 3x

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The algorithm iteratively selects the vertex with the smallest distance from the priority queue, updates the distances of its neighbors if a shorter path is found, and continues until all vertices have been processed. Finally, the calculated distances are printed for each vertex in the graph.

Here's a specific implementation of Dijkstra's algorithm in Python based on the given pseudocode:

python

Copy code

import heapq

def dijkstra(graph, start):

   # Initialize distances dictionary with a default value of infinity for all vertices

   distances = {vertex: float('inf') for vertex in graph}

   distances[start] = 0  # Set distance of the start vertex to 0

   # Create a priority queue to store vertices and their distances

   priority_queue = [(0, start)]

   while priority_queue:

       current_distance, current_vertex = heapq.heappop(priority_queue)

       # Skip if the distance to the current vertex is already greater than the calculated distance

       if current_distance > distances[current_vertex]:

           continue

       # Traverse through the neighbors of the current vertex

       for neighbor, weight in graph[current_vertex]:

           distance = current_distance + weight

           # Update the distance if a shorter path is found

           if distance < distances[neighbor]:

               distances[neighbor] = distance

               heapq.heappush(priority_queue, (distance, neighbor))

   return distances

# Define the graph

graph = {

   's': [('v', 1), ('w', 4)],

   't': [('v', 6), ('w', 3)],

   'v': [('s', 1), ('w', 2), ('t', 6)],

   'w': [('v', 2), ('s', 4), ('t', 3)]

}

# Specify the start vertex

start_vertex = 's'

# Run Dijkstra's algorithm

distances = dijkstra(graph, start_vertex)

# Print the calculated distances

for vertex, distance in distances.items():

   print(f"Distance from {start_vertex} to {vertex}: {distance}")

In this implementation, the dijkstra function takes a graph as input, represented as a dictionary where the keys are the vertices and the values are lists of tuples containing the neighbors and their corresponding edge weights. The function returns a dictionary with the shortest distances from the start vertex to all other vertices.

The algorithm utilizes a priority queue (implemented using a heap) to efficiently process vertices in order of their distances. The distances dictionary is initialized with infinity as the default value for all vertices except the start vertex, which is set to 0. The algorithm iteratively selects the vertex with the smallest distance from the priority queue, updates the distances of its neighbors if a shorter path is found, and continues until all vertices have been processed.

Finally, the calculated distances are printed for each vertex in the graph.

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Answer:

$250,000

Step-by-step explanation:

20000/8=1%=2500

2500*100=250000