- QUESTION
Analysis
•Accurate and complete decision tree analysis in Excel.
***I will attach example files of how do decision tree analysis in Excel**
Required:
•Length requirements = 2–3 pages minimum (not including Cover and Reference pages)
•Provide a brief introduction/ background of the problem.
•Complete and accurate Excel analysis.
•Written analysis that supports Excel analysis, and provides thorough discussion of assumptions, rationale, and logic used.
•Complete, meaningful, and accurate recommendation(s).
***Complete assignment details will be attached(SLP#4) In this assignment you will use the data from the analysis from SLP#3 which I will attach. I will attach the word file and excel file to assist with this assignment.***
| Subject | Business | Pages | 5 | Style | APA |
|---|
Answer
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Is the Market: Favorable or Unfavorable?
Introduction
Bayesian theorem has become an important parameter for decision making in the 21st century. The theorem has become an important aspect of problem-solving within a wide range of disciplines from medicine to engineering. In developed countries specifically, there has been a heightened growing interest in the use of Bayesian network in the management of resources (Ames et al. 2003).
Bayesian networks have offered great support to decision-makers operating in most intricate and uncertain domains. Bayesian theorem has enabled assembling of different information in a regular and coherent framework. The different information can be incorporated in the uncertainties inherent in natural systems and decision making.
The success of Bayesian theorem and network has arisen due to their simplicity as a modeling tool in decision-making as compared to other modelling. Bayesian models are graphical models that capture cause and effect in relationships through influence diagrams. Bayesian theorem makes use of probabilities to characterise the strengths of linkages between variables. Therefore, Bayesian theorem can be used to define both quantitative and qualitative information.
A proper continuation of this study cannot continue without understanding the difference between Bayesian network and Bayesian theorem. Bayesian network is a demonstration of a joint probability distribution of a set of random variables with a likely shared causal relationship. A Bayesian network consists of nodes signifying the random variables, edges between pairs of nodes representing the causal relationship of these nodes, and a conditional probability distribution in each of the nodes.
Bayer’s theorem can be understood in terms of a statement which will form the core of this assignment.
Bayes' Theorem is a probability theorem, which is used as means of understanding how the trueness of a probability is affected by a any new piece of evidence.
Where E is new piece of evidence and T is theory or hypothesis of interest.
Application
Given below is a task to be achieved.
These are the possibilities in this task from the need of the High Value, High Price smart phone (Pr = 0.4), or an Average Value, Average Price smart phone (Pr = 0.6).
From the decision tree, there are four possible outcomes:
Pr( Expert Says "wants high value" | Market demand is High value) = 85%
Pr( Expert Says "wants avg value" | Market demand is High value) = 15%
Pr( Expert Says "wants high value" | Market demand is Avg value) = 8%
Pr( Expert Says "wants avg value" | Market demand is Avg value) = 92%
These four can be represented in excel as will be shown below.
Using decision tree, the best possible outcome is shown with the
This depending on the value given gives the largest outcome. An alternate is given by ‘light blue line’.
Using Bayer’s theorem, the best possible outcome is average value with high market value (≈ 0.74).
Recommendation
Therefore production should be skewed towards making average value smart phones yet considering high market product.
References
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