Answer:
cool
Explanation:
coooooooooooooooooooooooooooool
Answer:
Why or why not? Yes, organizing is still a very important managerial function because work is separated, assembled, and coordinated with the help of organizing. Therefore allowing employees to work anywhere, anytime. Also, people still need to plan what to do at what time so as to ensure time maximation.
Credits to : assignmentexpert
Explanation:
Answer:
The answer is: The expected rate of return from this investment is 26.68%
Explanation:
We are given the following cash flows for this operation:
- Initial investment = -$24.50
- Cash flow 1 = $1.25 (dividend year 1)
- Cash flow 2 = $1.35 (dividend year 2)
- Cash flow 3 = $1.45 (dividend year 3)
- Cash flow 4 = $56.55 ($1.55 dividend year 4 + $55 stock's sales price)
Using an excel spreadsheet and the IRR function:
=IRR(value 1: value 5) =26.68%
where
- value 1 = -24.50
- value 2 = 1.25
- value 3 = 1.35
- value 4 = 1.45
- value 5 = 56.55
The answer should be : with what the organization is trying to accomplish.
Hope this helps !
Photon
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.
