Answer:
$60 million
Explanation:
The computation of the value of operations after the repurchase is shown below:-
Total corporate value = Value of operation + marketable securities
(5 × $15 million) = Value of operation + $15 million
$75 million = Value of operation + $15 million
Value of operation = $75 million - $15 million
= $60 million
We simply applied the above formula so that the firm's value of operations after the repurchase could come
It would be one of those fast food places and or being a nanny or a gilr for beinga butler u get the point ur welcome.
Answer:
$10,700
Explanation:
The unit product cost = $15 + $57 + $3 = $75
Sale revenue = $100 × 8,400 = $840,000
Less :Variable cost
Variable cost of goods sold = 8,400 × $75 = $630,000
Variable selling and administrative = 8,400 × $7 = $58,800
Contribution margin = $151,200
Fixed manufacturing overhead = $132,000
Fixed selling and administrative expenses = $8,500
Net operating income = $10,700
Answer:
D. trade-offs associated with financial decisions.
Explanation:
Opportunity cost is the cost of the next best option forgone when one alternative is chosen over other alternatives.
Let's assume Martin can produce either 5 jeans or 10 shirts in one hour. If Martin decides to produce jeans instead, his opportunity cost are the shirts he trades off when he decided to produce jeans.
I hope my answer helps you
Answer:
The options for this question are the following:
a. 1
b. 2
c. 0.5
d. 1.5
The correct answer is a. 1
.
Explanation:
Group analysis or grouping is the task of grouping a set of objects in such a way that the members of the same group (called a cluster) are more similar, in some sense or another. It is the main task of exploratory data mining and is a common technique in the analysis of statistical data. It is also used in multiple fields such as machine learning, pattern recognition, image analysis, information search and retrieval, bioinformatics, data compression and graphic computing.
Group analysis is not in itself a specific algorithm, but the task pending solution. Clustering can be done using several algorithms that differ significantly in your idea of what constitutes a group and how to find them efficiently. Classical group ideas include small distances between members of the group, dense areas of the data space, intervals or particular statistical distributions. Clustering, therefore, can be formulated as a multi-objective optimization problem. The appropriate algorithm and the values of the parameters (including values such as the distance function to use, a density threshold or the number of expected groups) depend on the set of data analyzed and the use that will be given to the results. Grouping as such is not an automatic task, but an iterative process of data mining or interactive multi-objective optimization that involves trial and failure. It will often be necessary to pre-process the data and adjust the model parameters until the result has the desired properties.
