Answer:
bureaucracy
Explanation:
According to my research on business strategies, I can say that based on the information provided within the question UPS is successful because of bureaucracy. This is a system of administration that has a clear hierarchy of authority, rigid labor, and inflexible rules and regulations, which allowed UPS to cement itself as one of the most successful delivery companies in the World.
I hope this answered your question. If you have any more questions feel free to ask away at Brainly.
Given:
400 shares of Google
399.75 per share
2% commission on purchase price.
400 shares * 399.75/share = 159,900
159,900 x 1.02 = 163, 098
The total to Bee Sting is $163,098
Answer:
D
Explanation:
Profit is Maximize when MR = MC
since MR=40 - 0.5Q
and MC= 4
Therefore:
40-0.5Q = 4
-0.5Q = 4 - 40
-0.5Q= -36
divide through by -0.5
Q = 72
since Q = 72
from Q = 160 - 4p
72 = 160 - 4P
-4p = 72 - 160
-4P = -88
divide through by -4
P = 22
Answer: See explanation
Explanation:
The following can be calculated from the information given:
Total Asset Fair Value which will be:
= Land + Building + Equipment
= 73200 + 268400 + 97600
= $439200
Recorder Amount will now be:
Land = 73200 / 439200 × 384300
Land = 64050
Building = 268400 / 439200 × 384300
Building = 234850
Equipment = 97600 / 439200 × 384300
Equipment = 85400
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.
