<span>
The form of currency that is no longer backed by gold is called money. The currency is not backed by gold because in 1971 people have became able to utilize </span><span>banknotes</span><span> as the only form of money. So, the money had no currency with any gold or silver backing and that is the reason why it is not backed.
</span>
Answer:
under applied by $1,000.
Explanation:
The formula is shown below:
Predetermined overhead rate = (Total estimated manufacturing overhead) ÷ (estimated direct labor-hours)
= $101,998 ÷ 67,992 hours
= $1.50
Now we have to find the applied overhead which equal to
= Actual direct labor-hours × predetermined overhead rate
= 70,000 hours × $1.50
= $105,000
So, the ending overhead equals to
= Actual manufacturing overhead - actual overhead
= $106,000 - $105,000
= $1,000 under-applied
Answer:
The correct answer is letter "C": sales minus costs of intermediate goods.
Explanation:
Value Added is used to describe the extra something a company does to a product that makes it worth more than the cost of its underlying parts. For economists, value-added is the <em>difference between the gross revenue for an industry</em> (sales) <em>and the sum of the labor, materials, and services </em>(intermediate goods) <em>purchased to produce the goods that generated the revenue.</em>
Answer:
The overhead for the year was $130,075
Explanation:
GIVEN INFORMATION -
ESTIMATED ACTUAL
Manufacturing overhead $132,440 $128,600
Machine hours 2800 2750
Here for calculating the overhead for the year we will use the following formula =
\frac{Estimated Manufacturing Overhead}{Estiamted Machine Hours}\times Actual Machine Hours
= \frac{\$132,440}{2800}\times 2750
\$47.3\times 2750 = \$130,075
Therefore the overhead for the year was $130,075
Please find full question attached
Answer:
Inferential statistics
Descriptive statistics
inferential statistics
descriptive statistics
Descriptive statistics
Inferential statistics
Explanation:
Descriptive statistics describes data and gives us a picture of what the data summary looks like using such things as mean and central tendency measures. Inferential statistics on the other hand aims to make predictions using the data based on data analysis such as collecting sample from population and constructing hypotheses to estimate outcomes for the general population. Example in the question, the first is inferential statistics as we make generalized predictions on batteries using data samples from the population of batteries of a particular type.
