Answer: c. quantity of coal produced will be greater than the socially optimal quantity
Explanation:
The Socially Optimal quantity is the level of production that takes into account the negative externality being produced by coal and weighs it against the marginal benefit it brings.
If coal produces the negative externality of polluting the environment here then the socially optimal quantity of coal will be less than the market equilibrium because the marker equilibrium would not have taken into account the negative externality and will lead to more coal being produced than should be.
Answer:
As a part of internal accounting controls, the activity called <u>RECONCILIATION</u> involves detecting and avoiding errors.
Explanation:
Reconciliation involves detecting and avoiding errors since it helps management to discover or detect any mistakes and errors, and can also help to understand why these errors occurred and how to prevent future mistakes.
Reconciliation is basically comparing two different accounting records and making sure that they match, e.g. reconciliation of bank account and cash balance. If you cannot reconcile the company's bank account with its cash balance, it means something is wrong. Is the mistake intentional? Why did it occur? How can you prevent it from happening again? Can it be solved?
Answer:
$537,000
Explanation:
Contribution margin is used to determine the profitability of a product. it is price less variable cost
Contribution margin = total sales - variable costs
$ 768,000 - $231.000 = 537,000
A/B Test is a method of measuring the effectiveness of altering one characteristic of a marketing asset such as a web page, a banner advertisement, or an email.
More about A/B Test:
In data science an elementary randomised control experiment is A/B testing. It is a method for contrasting two variations of a variable to see which performs better in a regulated setting.
Let's say, for example, that you run a business and want to boost product sales. You could divide the products in the aforementioned scenario into A and B parts. Here, A won't change, but you can significantly alter B's packaging. You now attempt to determine which is performing better based on the responses from customer groups who used A and B, respectively.
It is a hypothetical testing procedure in data science used to make judgments about population parameters based on sample statistics.
Learn more about data science here:
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