Answer:
$2 per unit per year
Explanation:
The calculation of the inventory carrying cost per unit per year is shown below:
Inventory Carrying cost per unit per year is
= Total Annual Inventory cost ÷ Economic order quantity
= $400 ÷ 200 units
= $2 per unit per year
It is computed By dividing the total annual inventory cost from the economic order quantity, in order to get the inventory carrying cost
Therefore, the first option is correct
Answer:
$335,428
Explanation:
The computation of the plane operating cost is shown below:
Plane Operating Cost = Fixed cost + (Variable cost per unit × quantity) + (Variable cost per unit × quantity)
= $41,490 + ( $2,839 × 101 flights) + ($23 × 313 passengers)
= $41,490 + $286,739 + $7,199
= $335,428
We only considered the planned activity as we have to compute the plane operating cost for the planning budget
Relationship capital is the answer. it is a form of
intellectual capital, which is the value derived from an organization's relationships
with customers, suppliers, and others who provide added mutual value for the
organization. It includes brand image and organization’s goodwill.
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.
