Answer:
This is made due to the application of the cost principle or historical cost concept.
Explanation:
The cost principle or historical cost concept states that the assets, equities, and liabilities are required to be recorded on the financial records on the basis of their original cost. Thus as the cash paid is ZMW 51,000, the same is required to be recorded on the balance sheet of the buyer.
Answer:
Segmentation
Explanation:
Market segmentation is a study that decides whether the company splits its members or populations into smaller categories based on factors such as age, wealth, personality features or actions. These divisions will also be used to tailor goods and ads to specific consumers.
In the case of health insurance providers, they use market segmentation to maintain the difference between individuals and decide about their premium, desire and other benefits.
Answer:
Bradford's estimated variable manufacturing overhead cost is $127,200
Explanation:
The cost function=$83,000+$12M
where M stands for machine hours required to produce the expected output in the month under review.
Each one-six unit case of Bradford's single product requires two machine hours,hence 5,300 cases would require 10,600 hours(5,300*2hrs).
Total estimated variable manufacturing overhead=cost per machine hour*expected number of machine hours
cost per machine hour is $12 as seen in the cost function
estimated variable manufacturing overhead=$12*10,600=$127,200
To put it simply, they have to develop a product that make people want to pay to acquire it.
Sellable product usually either :
- able to make people happy ( such as movies, music, Delicious food, etc) or
- Able to make people's life become easier ( such as Gadgets, consultation service, etc)
Answer:
<em>c. The reasoning of both Alfons and Mary suffers from the omitted variable problem</em>
Explanation:
The issue of omitted variables occurs as a result of mis-specification of a linear regression model, which could be either because the impact of the omitted variable on both the dependent variable is unclear, or the evidence was not accessible.
This causes you to omit the variable from your regression, resulting in over-estimation (upward bias) or underestimation (downward) of the influence of one of the other predictor variables.
