The firm is having a negative profit or loss of $51.75.
Explanation:
The cost function of a perfectly competitive firm is given as C = 64 + 15q + q2.
The marginal cost is MC = 15 + 2q.
The market price of the product is given as $22.
Each firm is producing 3.5 units in the short run.
The profit or loss to the firm is the difference between total revenue earned and the total cost incurred.
Total cost
= 64 + 15q + q2
= 64 + 15 3.5 +
= 64 + 52.5 + 12.25
= 128.75
Total revenue
= Price Quantity
= $22 3.5
= $77
Profits
= Total revenue - Total cost
= $77 - $128.75
= - $51.75
The firm is having a negative profit or loss of $51.75.
Explanation:
The cost function of a perfectly competitive firm is given as C = 64 + 15q + q2.
The marginal cost is MC = 15 + 2q.
The market price of the product is given as $22.
Each firm is producing 3.5 units in the short run.
The profit or loss to the firm is the difference between total revenue earned and the total cost incurred.
Total cost
= 64 + 15q + q2
= 64 + 15 3.5 +
= 64 + 52.5 + 12.25
= 128.75
Total revenue
= Price Quantity
= $22 3.5
= $77
Profits
= Total revenue - Total cost
= $77 - $128.75
= - $51.75
Graph A is not a Function as 4 each x, there are 2 y values.
Graph B is a Function as 4 each x, there is only 1 y value.
Mapping B is not a Function as x=5, there are 2 y values.
Mapping A is a Function as 4 each x, there is only 1 y value.
Graph A is not a Function as x=2, there are 2 y values.
Graph B is a Function as 4 each x, there is only 1 y value.
To explain what a home inspection does and does not include
Explanation:
hope this helps and
it may be to late but why not
-KB
Graph A is not a Function as 4 each x, there are 2 y values.
Graph B is a Function as 4 each x, there is only 1 y value.
Mapping B is not a Function as x=5, there are 2 y values.
Mapping A is a Function as 4 each x, there is only 1 y value.
Graph A is not a Function as x=2, there are 2 y values.
Graph B is a Function as 4 each x, there is only 1 y value.
To explain what a home inspection does and does not include
Explanation:
hope this helps and
it may be to late but why not
-KB
In the clarification portion below, the definition according to the received information is summarized.
Explanation:
Characterization:It is indeed a summary of general object characteristics in something like a target class and creates characteristic laws.
Discrimination:Just before predefined data types have been held to a different standard from everyone else, it's indeed bias which always happens.
Association:It's a mechanism that determines the possibility that objects in a set will co-occur.
Classification:It is indeed duction which attributes elements to target groups or classes in a set.
Prediction:It is solely dependent on either the interpretation of other similar values to classify data points.
Clustering:It has been used to position the components of the information through their corresponding classes.
Evolution Analysis:It would be for objects whose behavior varies throughout time to explain or design regularities.
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