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International Guidance and Controls
International Guidance and Controls
Let S be the cost of project lateness including direct and indirect costs. According to the case the
direct cost of lateness is $0.8m per month of lateness, and the indirect cost of lateness is the cost of
lost reputation. As an example, if the project is two months late and the cost of lost reputation is
estimated to be $2m, then S is $3.6m.
Let: SW Only = software only; HW Now = expand hardware now; Delay HW = delay hardware
decision; OT = project finished on time; Late = project finished late; FP = favorable software
progress in the first five months; NP = learning nothing new in the first five months; and UP =
unfavorable software progress in the first five months. ... Show more content on Helpwriting.net ...
For S ≥ $3.405m, choose HW Now.

Suppose that S is $1.6m. Now the decision tree is what you see in Figure 3. The overall
recommendation is: Choose Delay HW. If FP or NP, then choose SW Only; if UP then choose HW
Now.
The expected cost of the optimal decision is 3.2906. The risk profile is:
SW Only
Cost (million $) Probability
3 0.8
4.6 0.2
HW Now
Cost (million $) Probability
3.5 1
Delay HW
Cost (million $) Probability
3 0.744
3.75 0.14
4.6 0.116
Calculation of EVPI: Suppose that S is $1.6m and we want to compute the EVPI (expected value of
perfect information) where the information concerns whether the project would be late or not. We
need to consider two scenarios: one where no information is available, and the other where perfect
information is available. Note that the Delay HW choice can be ignored in this analysis because
delaying the hardware decision is akin to collecting some information on whether the project would
be finished on time or not.
When no information is available the decision tree is as shown in Figure 4. The expected cost is
$3.32m. When perfect information is available the decision tree is as shown in Figure 5. The
expected cost is $3.1m. With perfect information the expected cost goes down by $3.32 – $3.1m =
$0.22m = $220,000. Hence, EVPI = $220,000. We can also calculate EVPI by first calculating the
VPI of each piece of perfect information. Note
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Decision Tree Induction & Clustering Techniques in Sas...
International Journal of Management & Information Systems – Third Quarter 2010
Volume 14, Number 3
Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And
IBM Intelligent Miner – A Comparative Analysis
Abdullah M. Al Ghoson, Virginia Commonwealth University, USA
ABSTRACT Decision tree induction and Clustering are two of the most prevalent data mining
techniques used separately or together in many business applications. Most commercial data mining
software tools provide these two techniques but few of them satisfy business needs. There are many
criteria and factors to choose the most appropriate software for a particular organization. This paper
aims to provide a comparative analysis for three ... Show more content on Helpwriting.net ...
In this way, decision trees provide accuracy and explanatory models where the decision tree model
is able to explain the reason of certain decisions using these decision rules. Decision trees could be
used in classification applications that target discrete value outcomes by classifying unclassified
data based on a pre–classified dataset, for example, classifying credit card applicants into three
classes of risk, which are low, medium or high. Also, decision trees could be used in estimation
applications that have continuous outcomes by estimating value based on pre–classified datasets,
and in this case the tree is called a regression tree, for example, estimating household income.
Moreover, decision trees could be used in prediction applications that have discrete or continuous
outcomes by predicting future value same as classification or estimation, for example, predicting
credit card loan as good or bad. 2.1 Decision Tree Models
Decision tree models are explanatory models, which are English rules so they are easy to evaluate
and understand by people. The decision tree model is considered as a chain of rules that classify
records in different bins or classes called nodes [1]. Based on the model 's algorithm, every node
may have two or more children or have no child, which is called in this case leaf node [1]. Building
decision tree models requires partitioning the pre–classified dataset into three parts,
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Valuing Project Achieve
Evaluation of Financial Information Syllabus (Subject to minor changes) Spring 2012 Prof. Anna
Scherbina
UC Davis Graduate School of Management Office: 3212 Gallagher Hall Course Focus
Tel: 530.754.8076 e–mail: ascherbina@ucdavis.edu
We will learn how to use financial information to value firms, projects, and securities in a wide
variety of industries, including real estate. The course will be based entirely on the Harvard Business
School case studies and will focus on learning techniques of financial analysis, selecting an
appropriate valuation model, analyzing the quality of financial data, finding an appropriate discount
rate, and forecasting financial variables and cash flows. Corporate Finance course is strongly
suggested as ... Show more content on Helpwriting.net ...
Topics covered: buy or rent decision, real estate markets, forecasting
Session 2: CASE: Health Development Corporation Topics covered: own or lease decision, the use
of multiples
Session 3: CASE: Toy World, Inc. Topics covered: forecasting, production methods, balance sheet
risks
Session 4: CASE: Ocean Carriers Topics covered: cash flow forecasting, macro forecasting
Session 5: INTERACTIVE LECTURE and AN IN–CLASS EXERCISE (please bring your laptops):
Forecasting macro variables using international equity markets data CASE: Kerr–McGee Topics
covered: hostile takeovers, real options
Session 6:
Session 7: CASE: Merck & Company: Evaluating a Drug Licensing Opportunity Topics covered:
decision trees, probability trees, sunk costs, real options
Session 8: CASE: Valuing Project Achieve Topics covered: subscriber models, DCF valuation,
forecasting
4
Session 9: CASE: NetFlix.com, Inc. Topics covered: subscriber models, forecasting Homework
assignment due Session 10: INTERACTIVE LECTURE and AN IN–CLASS EXERCISE (please
bring your laptops): Asset Bubbles in Equity and Real Estate Markets
CASE QUESTIONS: Module 1 Session 1 Case: Stedman Place Case Questions: 1. What is the cost
of renting? 2. What is the cost of buying? 3. Identify the key assumptions and key unknowns that
influence the buy–versus–rent decision.
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Risk Management Essay
RISK MANAGEMENT ESSAY
The following essay has been written by analyzing the risks associated from the construction
managers/ project managers' point of view. Citing the possible risks associated while working on
international or varied geographical location. Risks are associated with almost all levels of the
project life cycle and is mutually shared and mitigated by all parties employed within the
construction industry. There are many evidences to state that poor risk mitigation leads to poor
performance and hence establish risk management processes and practices are required to be
adhered to in order to turn any project's outcome into a success.
The 2000 edition of the Guide to the Project Management Body of Knowledge (PMI,2000) states ...
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New entrants who set up similar projects may have access to long term loans at the prevailing rate of
interest which may be cheaper. In such a situation, projects that were implemented with high cost
borrowings will find it difficult to compete with the new entrants.
On the other hand if the interest rate increases in the future, the interest on working capital finance
(which normally carries a floating interest rate) increases which will result in lower profit margins
than estimated at the time of project appraisal. interest rate risks can be managed to some extent by
entering into interest rate hedging agreements like 'interest cap', 'interest swap' etc.
13 | P a g e
 EXCHANGE RATE RISK
Exchange rate risk, also called as 'currency risk' is the risk arising from currency fluctuations.
Volatile exchange rates can reduce cost and productivity advantages gained over years of hard work.
Firms exposed to international economy face this risk. When a firm has already committed to a
foreign currency denominated transaction, the firm is exposed to a exchange rate risk. The firm will
incur a
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Data Mining is a Technique Used to Clarify and Classify Data
Data Mining is a technique used in various domains to give meaning to the available data and
different types of Data to be handled like numerical data, non–numeric data, image data...etc. In
classification tree modelling the data is classified to make predictions about new data. Using old
data to predict new data has the danger of being too fitted on the old data. In this we evaluated
different types of data to be collected from UCI repository for classify the data using the different
classification algorithms J48, Naive Bayes, Decision Tree, IBK. This paper evaluates the
classification accuracy before applying the feature selection algorithms and comparing the
classification accuracy after applying the feature selection with learning algorithms.
1. Introduction
As computer and database technologies develop rapidly, data accumulates in a speed unmatchable
by human capacity of data processing[2]. Data mining as a multidisciplinary joint effort from
databases, machine learning and statistics, is championing in turning mountains of data into nuggets.
Researchers and practitioners realize that in order to use data mining tools effectively, data
processing is essential to successful data mining.PrimitiveThese are features which have an
influence on the output and their role cannot be assumed by the rest.[1]
Feature selection can be found in many areas of data mining such as classification, clustering,
association rules and regression. For example, feature selection is
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Supplier Selection
Introduction
The evaluation and selection of suppliers, structuring the supplier base is an important task in any
organization. It assumes utmost importance in the current scenario of global purchasing. Every
Organization especially manufacturing organizations need to have a Supplier evaluation matrix or
model in place. This paper tries to bring in a typical
Supplier Evaluation Framework, which blends with company's basic values, and help in establishing
a Strategic sourcing policy. It also outlines ways and means to reward a supplier and establish long–
standing relationships with suppliers.
Vendor selection range of criterions
Today's consumers demand cheaper, high quality products, on–time delivery and excellent after–sale
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* Ability to meet current and potential capacity requirements, and do so on the desired delivery
schedule. * Financial stability. * Technical support availability and willingness to participate as a
partner in developing and optimizing design and a long–term relationship. * Total cost of dealing
with the supplier (including material cost, communications methods, inventory requirements and
incoming verification required). * The supplier 's track record for business–performance
improvement. * Total cost assessment.
Now when the agreement on the business and vendor requirements had been compiled, the team
now must start to search for possible vendors that will be able to deliver the material, product or
service. The larger the scope of the vendor selection process the more vendors you should put on the
table. Of course, not all vendors will meet the minimum requirements and the team will have to
decide which vendors you will seek more information from.
Vendor evaluation range of criterions
The areas that company chooses to measure and manage and the criteria used will be a direct result
of the company 's goals and strategy and the objectives for the supplier performance management
program. There are a wide variety of areas of supplier performance that may be measured. It is
important to select the ones that are
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The Adversarial Risk Analysis Approach
Source: Figure 3 (Rios and Insua, 2012) Source: Figure 4 (Rios and Insua, 2012)
Source: Figure 5 (Rios and Insua, 2012) Source: Figure 6 (Rios and Insua, 2012)
The Adversarial Risk Analysis Approach relaxes the common knowledge assumption in order to
make this model more realistic. If the Defender's decision problem is a standard decision analysis
problem, shown in Figure 3, with the Attacker's decision node regarded as a random variable. Then
her decision tree in Figure 4 illustrates the uncertainty about the Attacker's decision by replacing A
(in a square, Fig 3) with A (in a circle, Fig 3). (Rios and Insua 2012)
Once the Defender has already assessed pD(S | d, a, v) and uD(d, s, v), she needs pD(A | d), which is
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The Defender's decision is illustrated as a random variable as it is not under control in the Attacker's
analysis. The arrow from D (in a circle, Fig 5) to A (in a square, Fig 5) in the influence diagram
demonstrates that he will know the Defender's decision while he has to decide. The Defender's
private information v, is not known by the Attacker, therefore his uncertainty is demonstrated
through a probability distribution pA(V), illustrating the Attacker's previous beliefs about the
Defender's private information. Assuming the Defender analyses the Attacker's decision, knowing
that he is an expected utility maximiser and uses Bayes's rule to discover about the Defender's
private information from monitoring of her defence decision. Consequently, the arrow in the
influence diagram from V (in a circle, Fig 5) to D (in a circle, Fig 5), represents probabilistic
dependence, is to be inverted to acquire the Attacker's subsequent beliefs about v: pA(V|D=d), yet to
acquire this it is needed to assess pA(D|v). (Rios and Insua 2012)
If the Defender knew the Attacker's utility function uA(a,s,v) and the probabilities pA(S|d,a,v) and
pA(V|d), she could predict his decision a*(d) for any d ∈ D by solving backwards the tree in Figure
6, followed by computing his expected utility ψA.
– Compute at chance node S: ψA(d,a,v) for each (d,a,v) as in Equation (2).
– Compute for
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The Decision Tree Algorithms And Grows The Tree By...
This is a pedagogical algorithm, which extracts the rules in the form of decision trees. This is similar
to most of the decision tree algorithms and grows the tree by recursive partitioning. At every step it
stores a queue of leaves that can be further expanded to sub trees and this process is repeated until a
stopping condition is met. Traditional decision trees methods have a limited number of training
observations. So they only have fewer number of observations to decide upon the split and leaf node
class labels but on the other hand, Trepan re–labels the original observations to the classifications
made by the network. And the relabeled data will be used for the tree growing process. Additionally
it can also add extra data points by mimicking the behavior of the network. It uses the network as an
oracle to answer the classification queries about the newly generated data points. This way it can
make sure that whenever a split node or leaf node class decision is made there are at least S_(min )
number of data points. Where S_(min )is a user specified number. Whenever we generate new data
points at any particular node, we have to make sure that they satisfy all the constraints from root to
the current node. One of the approach to distribute the data points over a network is to employ
uniform distribution, but Trepan takes into account the distribution of data i.e. at each node it
estimates the marginal distribution of data. If the data at the input is continuous then it
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The Cost Effectiveness Of A Drug Or Treatment
Rising healthcare costs are a growing concern among individuals, employers, and the federal
government. The national conversation on how to best control those costs has forced many drug
manufacturers to reevaluate the economics of new, expensive drugs and therapies. Now more than
ever, the need to evaluate outcomes and costs associated with alternative treatments has never been
greater.
Understanding the cost effectiveness of a drug or treatment can be a challenge. Clinical trials are
traditionally performed on subsets of the population in tightly controlled environments for a
relatively short time. They are primarily responsible for evaluating treatment efficacy. But pressure
to control healthcare costs has increased the emphasis on ... Show more content on Helpwriting.net
...
Chance nodes (circles) depict the possible consequence – positive or negative – of the decision.
They are referred to as transition states. Transition probabilities are assigned to each transition state
and they must always sum to one. Triangles indicate the point at which the analysis ends and the
health impact and/or costs of each consequence is quantified. When decision tree analysis is done at
the same time as the clinical trial, the payoff may also be expressed as utilities. Utility can be
described in numerous ways. For example, as a percentage of full health. A value of 0.7 corresponds
to a person living at 70% of full health. Another way to express utility is quality adjusted life years
(QALY). Expected value of each therapy is calculated by multiplying the payoff (dollars, percent,
QALYS etc.) with the probability of occurrence for every possible transition state.
While decision trees are simple to comprehend, complicated real–world scenarios cannot be
adequately modeled with basic decision tree analysis. The tree cannot model repetitive events or
transitions back and forth between two states. To model repetitive events or transitions backward
would require numerous repetitive transition states. Trying to create a path for every possible
scenario can quickly lead to a complicated, unmanageable decision tree.
Another inherent limitation of decision tree analysis is its stagnant nature. Model conditions, such as
transition probabilities or costs, are not
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What Is Offline And Online Scale Computation?
3.1 Offline and online scale computation For different values of κ the multiscale basis changes as
the pde solution changes. To address this issue offline–online computation is used where in offline
stage for representative values over a grid of κ's the offline basis and the corresponding linear space
is constructed. The online basis then constructed solving a local problem for each candidate κ∗,
using the principal component direction of the spectral problem. The mathematical details of the
online–offline technique can be found in Guha and Tan (2016), Efendiev et al( 2011). For a set of
values κ1,...,κN solving local eigen value problem to detect the dominant scale (inverse of the eigen
value) a offline space is constructed with basis ... Show more content on Helpwriting.net ...
In particular κ ∼ GP(K) and log(κ(x)) = ‫ﰅ‬ξ √λ e (x), for x ∈ D where K is covariance kernel on D
with iii eigen values λi and eigen vectors ei(x) and ξi ∼ N(0,1). 13 The goal is to derive the
posterior distribution of κ's or equivalently ξi's given observation and the model. Posterior for κ The
posterior for κ can be written as Π(κ|Y ) = p(Y |κ)π(κ) where p(Y |K) is the likelihood of Y given κ
which is a normal distribution for the above error distribution assumption around the solution K(u,κ)
from the pde. This posterior is not closed form and Metropolis–Hastings MCMC sampling is needed
to draw sample from κ. The likelihood computation involves solving for κ and this solution would
be calculated using basis function (multiscale basis for example). Using simple coarse scale basis
function that has a bilinear form in the coarse grid and zero outside the coarse region, gives a low
resolution solution that can be used to filter out the bad proposal value in MCMC set up. 3.3
Multilevel sampling with residue information Selecting the additional basis with this model based
distribution the randomized forward solution can be incorporated into a multilevel MCMC
methodology (Dodwell et al., 2014). Two level sampling is proposed for convenience but additional
levels can be used. Two level sampling are done on the coarse level and fine scale; in coarse scale
using coarse scale basis and in the fine level using multiscale basis.
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The Probability Of Wining In The Tic-Tac-Toe Game
The Goal
My goal is to find the probability of wining in tic–tac–toe game given that you make the first move.
To obtain hypothesis bases on my goal I have to state some conditions and facts on the game. They
are: 1) There are 362, 880 ways of placing O's and X's. 2) When X make first move, possibility of X
winning is 131,184, O winning is 77, 904, and 46, 080 tied games (Source:
http://en.wikipedia.org/wiki/Tic–tac–toe). After eliminating rotations and/or reflections of other
outcomes, there are only 138 unique outcomes. X won 91 times, O won 44 times and 3 ties (Source:
http://en.wikipedia.org/wiki/Tic–tac–toe). Basically, the win of X is the concept. There are 8
possible ways of creating three X in row. Based on this my hypothesis ... Show more content on
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After that I performed ESX supervised learning with all data as training data as shown below: Then
I performed another mining session with only first 658 instances as training data and other 300 as
testing data in order to evaluate the model. The parameter window is shown below. Apart from
supervised learning, several other mining techniques are used to evaluate the data. They are:
Mangrove, a freeware which generates decision tree and classification tree using excel file. All of
them will be described more in detail under the evaluating the results division.
Interpretation of the results (from Data Mining)
When talking about interpreting the results, first we have to check whether the formed classes are
solid one. To do this we have to check whether the class resemblance score which should be higher
than the domain resemblance score. In this case both Positive and Negative class has score higher
than the domain resemblance score. They are only slightly higher because of the nature of the values
of the attribute. The scores are shown below. When we look at the domain statistics for the
categorical attributes we can see the predictability score for all the attribute value X is more than the
predictability score of other two possible values of O (other player move) and b (blank). The scores
are shown below. When we examine the class individually I found that the attribute value highly
sufficient for being class membership is M–M = X, and the attribute
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Decision Analysis Study
Decision Analysis Study
Decision Analysis Study
Introduction
This paper will Be providinG a memo that includes many Tasks related To project planning and
operations management. All memos are present accordingly to the separated tasks discussed. We
will be using the case study of "Shuzworld". As the operations consultant for Shuzworld, we will be
following all the tasks and then will provide Recommendations by analyzing the problems given in
the task prompts. We will also apply the appropriate decision analysis tool to make reliable and valid
Recommendations.
Task 4
Part A
In this task we will be providing whether Shuzworld should build the proposed stand–alone store,
the strip mall store, or not proceed with construction, by ... Show more content on Helpwriting.net ...
The profitability problem is one of the most important problems of future profitability when opening
a new store process and is the important content of the course of operation management. By
recommending the use of this crucial tool, we need to understand, that this will give Shuzworld a
direction, to which one of the three options is the best (the stand–alone option, opening of a brand
new store or to do nothing now) for the most cost–effectively and profitability option.
Also I am recommending this tool, because the decision tree analysis process, which are of great
importance in the industrial production of high quantity standardized products (Shoes, sandals, etc)
and lately has gained importance in the low volume production of customized products. Due to the
high capital requirements when looking for the opening of a new store, decision tree analysis is of
great relevance for the manufacturing business. Because of it, this attracted the attention of the
manufacturing industry, who tried to support practical cost–effectively analysis by using the proper
profitability models.
Now, we will analyzed, which one of the three options is the most cost effective and profitable, than
the other two. To finally be able to make the proper recommendation. We need to find out, which
one of the exact EMV, in which process (option), will become more cost–effective and
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Research Assignment: Data Structures and “Space Quest”
Research Assignment: Data Structures and "Space Quest"
"Space Quest" is a game about a lone traveller, flying through the cosmos. The journey is not a quite
one, however, as there are alien Bounty Hunters trying to take down the traveller. The player takes
the role of the traveller, and their aim is to avoid an AI controlled alien ship, destined to crash into
the player. Unfortunately for the player, it isn't exactly over once the first ship has been
outmanoeuvred: there are still other alien spaceships waiting. Luckily for the traveller, the Bounty
Hunter's identity is published throughout the galaxy, so devising a strategy will be easy work. For
the implementation of 'Space Quest', two data structures were required namely, a Tree, and a ...
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The enemy AI needs to make decisions based on where the player is located, and attempt to move
towards the player. This can only be accomplished through a series of 'Yes and No', or 'True and
False', trials. The AI will ask questions such as, "Is the player above me? Is the player below me?"
and so forth, therefore, the tree will need to generate and decide on finding the best path to take to
approach the player. In order to achieve this, a data structure known as a Decision Tree is required.
According to de Ville & Neville (2013), a decision tree can be defined as "a simple, but powerful
form of multiple variable analysis." Decision Trees were first introduced over fifty years ago, and
are still being refined today to provide new functionality for dealing with the newer code–
development related issues we might encounter (de Ville & Neville, 2013). Since decisions trees are
essentially binary trees, they also operate in O(log(n)) time complexities for best and worst case
scenarios, and O(n) for space. Decision trees are generated by algorithms which determine possible
ways to branch–off data depending on the end result of a set question. From this, the aim of the tree
would be to predict the probability of a specific outcome. In terms of Space Quest: where will the
player be next, and how can they be reached? For each node in the implementation, the AI will ask
itself if the defined condition mentioned earlier yielded either a true or false
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Analysis Of Genovus Biotechnologies ( Genovus )
Title
Genovus Biotechnologies (Genovus) is an infant biomedical company, which is currently working
to procure seed A funding. Each member of the Genovus leadership is working to propel the
company into existence while holding other jobs to pay basic life expenses. There is a small budget
for information technology tools such as email, calendar, and videoconference. The Genovus
leadership team needs to decide what information technology options will streamline and simplify
the work for the four–person business. This paper reviews two decision making tools, mind mapping
and decision tree analysis, and how these two tools may help the Genovus leadership team
determine the toolsets needed at this time. Mind mapping is a brainstorming tool. It enables the tool
user to engage with concepts in at a deeper level, thereby encouraging longer lasting learning about
a topic (Davies, 2011). When concepts are complex, the mind mapping process allows linking like
concepts together in a nonlinear model, assisting the user in uncovering dependencies between
concepts. Additionally, the visualization of data facilitates the user's ability to quickly think through
multiple ideas spontaneously (Davies, 2011). The process of mind mapping increases the quantity
and quality of the ideas, while facilitating critical thinking (Luh, Ma, Hsieh, & Huang, 2012). When
performed in a team setting, the visualization, and free flow of thinking bring out a variety of ideas
in a group (Luh et al.,
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Data Mining Techniques And Their Applications
Data Mining Techniques and Their
Applications
Deepika Sattu, 800721246, dsattu@uncc.edu
Abstract– Data mining is logical process that is used to extract or "mining" large amount of data in
order to find useful data [2]. Knowledge discovery from Data or KDD is synonym for Data
Mining[13].There are many different types of techniques that can be used to retrieve information
from large amount of data. Each type of technique will generate different results. The type of data
mining technique that should be selected depends on the type of business problem that we are trying
to solve.
Keywords: Clustering, Decision Trees, Classification,
Prediction
I. INTRODUCTION
Data is very critical for any organization. In an organization every by year massive amounts of data
will be created and how fast your business reacts to that important information determines whether
you succeed or fail. The big problem is how we efficiently handle the 3 V's of Big Data.
3V's of Big Data are Volume, Velocity, and Variety.
Volume: Amount of Data
Velocity: Speed at which the data is being processed.
Variety: Usage of data in various forms. i.e., graph, tree, nodes. [14]
Now a day's data is in Exa Bytes and Zeta Bytes. It is impossible to manually analyze and extract
data. In some Clusters data is increasing where as in others it is decreasing. There are various Data
Mining techniques such as Association, Clustering, and Prediction that is used to retrieve data from
Large databases (Big
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Avalanche Corporation
Avalanche Corporation
Decision Analysis and Strategic Recommendation
Table of Contents Table of Contents 1 Overview 2 Question 1: Production Strategy 2 Question 2:
Sensitivity Analysis 3 Question 3: Influence of Outside Vendor 5 Question 4: Alternative Risk
Profiles 6 Question 5: Are Fantastic Forecasters Worth It? 7 Conclusions 7 Appendix 8 Figure A:
Precision Tree (Question 1) 8 Figure B: Cost Calculation Table 9 Figure C: Profit Calculation Table
9 Figure D: Tornado Graph 10 Figure E: Tornado Graph Data 11 Figure F: Spider Graph 12 Figure
G: Decision Tree (No Outsourcing Available) 13 Figure H: Sensitivity of Decision Tree (with Data)
14 Figure I: Strategy Region (with Data) 15 Figure J: ... Show more content on Helpwriting.net ...
Precision Tree calculated the EMV for medium and high production which resulted in an outcome of
$1,025,000 and $850,000 respectively. Therefore the decision with the highest predicted payoff
would be to choose the batch flow and to produce 15,000 items.
Question 2: Sensitivity Analysis In order to get a better understanding of what the key inputs or
most sensitive input variables were, a sensitivity analysis was conducted on all of the inputs
involved in this case. For the sensitivity analysis an arbitrary number of 25% was chosen to vary the
inputs in order to analyze their effects on the EMV. For the sake of clarity and simplicity we then
decided to focus on several of the main inputs (and combinations of these inputs) that had the
greatest effect on the EMV or were deemed necessary for analysis. These were: * Sales Price *
Quantity of High or low Demand * Probability of High Demand * Outsource Cost * Clearance Cost
As we can see from the tornado graph [Figures D & E] the EMV of the decision tree was most
sensitive to the variations in the sales price of the avalanche racer. An increase/decrease of 25% of
the sales price of the Avalanche Racer led to an approximate increase/decrease of the EMV by 90%
which is statistically significant. This was further supported by the spider graph [Figure F], in which
the sales price was also shown as having the
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Decision Tree Case Study
Decision tree analysis
Decision tree analysis known as an analytical tool applied to decision–making under condition of
uncertainty, also clarifying where there are many possible outcomes for various alternatives and
some outcomes are dependent on previous outcomes. However, decision tree will present as a
diagram by showing the relationship among possible courses of action, possible events and the
potential outcomes for each course of action in the decision (Drury, 2012). So decision tree analysis
is useful for merchant navy company to understand in what direction their chance events are and
what their values in terms of profits and losses are for each of the two tooling alternatives, also
visualize the outcomes of different prospects ... Show more content on Helpwriting.net ...
The marketing management thinks that market shipping new service nationally successful that the
expected profits (excluding the cost of the market study) will be ¥1,600,000 however if the market
shipping new service nationally failure then it expected loss of ¥700,000(excluding the cost of the
market study). However absence of market study, there are equal chances of national success and
national failure after if China Shipping (Group) Company decide to market nationally. Therefore
now the marketing management has to determine the best strategy that China Shipping (Group)
Company should adopt.
Excel
According to the decision tree above, the optimal decision for China Shipping (Group) Company is
to carry out test market and then market nationally if national success then the company will have
return in expected profit of ¥654,000 compared to do not conduct test market only have expected
profit of ¥450,000.
Sensitivity analysis
Assuming the probability of national success after the market study will decrease by 15% and the
probability of national success without market study will increase by 10%. If a local success is
observed, the new probability will decrease from 80% to 65% that new shipping service will be
national success. If a local failure is observed, the new probability will decrease from 30% to 15%
that new shipping service will be will be national success. However absence of market study and
immediately market
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Learning Tree Executive Summary
Nowadays, in the education industry has been a highly competitive industry and many new
competitors are entering the market. Learning Tree International, Inc. was originated in 1974 and
headquartered in Reston, Virginia, it is considered one of the well–known companies in the
education industry. According to Yahoo Finance, Learning Tree Inc. has 393 full–time employees.
Learning Tree International, Inc. (LTRE), operates in the education and training services industry
(SIC code: 8200). The services that the company provides are training and education for commercial
and government information technology and management professionals. Also, it known for its
spread worldwide and that they offer their services online through what they call "Learning ... Show
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The return on equity ratio for the company is –66.01%, K12's is 3.7%, and the industry is 21.35%,
This is another indication that the company is not operating well and that the shareholders are
currently not earning from their investments in the company. On the other hand, the competitor –
K12– is also operating poorly with comparison to the industry's average percentage, but it is
performing better than Learning Tree International, Inc. Also, using another profitability ratio, which
is return on assets. The company's ratio is –13.3, K12's ratio is 2.74, and the industry's ratio is 11.85;
consequently, the company has a negative percentage while the percentages for the industry and
K12 are positive. So the company is not employing its total asset to generate profit as the same as
K12. In short, when comparing the profitability ratios of the company with industry and K12, it
shows that the company is in unstable condition with its investors. Moreover, the earning per share
for Learning Tree International, Inc. over the last three years are: $–0.90 on 9/12, $–0.66 on 9/13,
and $–0.50 on 9/14. Even though the company still has a negative EPS, but it has been increasing
from year to year. In addition, the price/sale ratio is a ratio that measure the stock price with the
annual sales and could be a good comparison between companies. The company's P/S ratio is 0.27,
K12 is 0.77, and the industry is 1.3, so we can tell that the company is clearly below its competitor
and its
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The Decision Tree Method For Intrusion Detection System
Abstract
There are many risks in using the internet irrespective of its popularity. These risks are network
attack, and attack method which vary every day. This research is aim to compare decision tree
method for intrusion detection. As intrusion detection is one of the major research problem in
network security. Tradition intrusion detection system contain a number of problems, such as low
performance, high false negative rate, low intelligent level. In this research work we compared
effectiveness of decision tree method in Intrusion Detection System. We also compare the detection
rate, false alarm rate for different types of attack.
1.0 Background
Intrusion Detection Systems (IDS) are software or hardware designed to automatically monitor
activities within a network of computers and identify any security issues . IDS have been around for
at least 30 years since increased enterprise network access produced a new challenge, the need for
user access and monitoring. As day–to–day operations grew increasingly dependent upon shared use
of information systems, levels of access to these systems and clear visibility into user activity was
required to operate safely and securely.
Many of the initial headway on IDS was made within the U.S. Air Force. In 1980, James P.
Anderson, an innovator of information security and member of the Defense Science Board Task
Force on Computer Security at the U.S. Air Force, produced "Computer Security Threat Monitoring
and Surveillance," a
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A Survey On Data Mining Classification Algorithms
A Survey on Data Mining Classification Algorithms
Abstract: Classification is one of the most familiar data mining technique and model finding process
that is used for transmission the data into different classes according to particular condition. Further
the classification is used to forecast group relationship for precise data instance. It is generally
construct models that are used to predict potential statistics trends. The major objective of machine
data is to perfectly predict the class for each record. This article focuses on a survey on different
classification techniques that are mostly used in data–mining.
Keywords: Data mining, Classification, decision tree, neural network.
1. INTRODUCTION
Data mining is one of the many ... Show more content on Helpwriting.net ...
Classification contains finding rules that partition the data into disjoint groups patterns and process.
The goal of classification is to evaluate the input data to develop a precise. Explanation or model for
each class using the features by using the present data.
2. ARCHITECTURE OF DATA MINING
Data mining and knowledge discovery is the name frequently used to refer to a very
interdisciplinary field, which consists of using methods of several research areas to extract
knowledge from real–world datasets. There is a distinction between the terms data mining and
knowledge discovery which seems to have been introduced by [Fayyad et al.1996].the term data
mining refers to the core step of a broader process, called knowledge discovery in database.
Architecture of data mining structure is defined the following figure.
3. DATA MINING PROCESS
 Data cleaning
 Data integration
 Data selection
 Data transformation
 Pattern evaluation
 Knowledge presentation.
Data cleaning: Data cleaning or data scrubbing is the process of detecting as well as correcting (or
removing) inaccurate data from a record set. It handles noisy data it represents random error in
attribute values. In very large dataset noise can come in many shapes and forms. And irrelevant data
handles the missing and unnecessary data in the source file.
Data integration: Data integration process contains the data from multiple sources.
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Improving Decision Tree Performance Methods
There are several improvement methods are available to improve decision tree performance in terms
of accuracy, and modelling time. Since experimenting with every available method is impossible,
some of the methods are selected that are proven to increase decision tree performances. Selected
improvement methods and their experimental setups are presented in this chapter.
4.1 Correlation–Based Feature Selection
Feature selection is a method used for reducing number of dimensions of a dataset by removing
irrelevant and redundant attributes. Given a set of attributes F and a target class C, goal of feature
selection is to find a minimum set of F that will yield highest accuracy (for C) for the classification
task. Although ... Show more content on Helpwriting.net ...
Also, method is performing well for C4.5 algorithm is likely to perform well for ID3 algorithm.
Previous studies show that CFS method increases accuracy for CART algorithm although not as
much as the C4.5 algorithm does (Doraisamy et al., 2008).
CFS uses a search algorithm and feature evaluation algorithm which uses a heuristic that measures
"goodness" of attributes subsets. Hall and Smith (1998) define this goodness heuristic as "Good
feature subsets contain features highly correlated with the class, yet uncorrelated with each other."
Equation 1 below shows heuristic formula. G_x=(k¯(r_ci ))/√(k+k(k–1)¯(r_ii ' ))
Where G_x is the heuristic of goodness of an attribute subset x that contains k features, ¯(r_ci ) is
average attribute–class correlation which points predictive power of the attribute subset to a class,
and ¯(r_ii ' ) is average attribute inter–correlation that indicates the redundancy among attributes.
A version of correlation–based attribute selection to be included in experiment setup is called Fast
Correlation–Based Feature Selection (FCBF) that initially developed by Yu and Liu (2004). This
algorithm is preferred over other available correlation–based attribute selection algorithms since
while other implementations of CFS using forward–sequential or greedy search methods (e.g.
MRMR/CFS developed by Schoewe,
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Data Mining Techniques And Their Applications
Data Mining Techniques and Their Applications in Financial Data Analysis
Deepika Sattu, 800721246, dsattu@uncc.edu
Abstract– Data mining is a logical process that is used to search through large amount of data in
order to find useful data [2].There are many different types of analysis that can be done in order to
retrieve information from big data. Each type of analysis will have a different impact or result.
Which type of data mining technique you should use really depends on the type of business problem
that you are trying to solve.
Keywords: Clustering, Decision Trees, Classification, Prediction
I. INTRODUCTION
Data is very critical for any organization, industry or business process. Data which was in gigabytes
or terabytes ... Show more content on Helpwriting.net ...
Here are the few typical cases:
Design and construction of data warehouses for multidimensional data analysis.
Loan payment prediction and customer credit policy analysis.
Classification and clustering of customers for targeted marketing.
Detection of money laundering and other financial crimes.[6]
II. DATA MINING TECHNIQUES
Data mining is a logical process that is used to search through large amount of data in order to find
useful data [2].There are many different types of analysis that can be done in order to retrieve
information from big data. Each type of analysis will have a different impact or result. Which type
of data mining technique you should use really depends on the type of business problem that you are
trying to solve. Different analyses will deliver different outcomes and thus provide different insights
[1].
Below are the three steps involved to make certain decisions for development of their businesses [2]
1. Exploration: In the first step of data exploration data is cleaned and transformed into another
form, and important variables and then nature of data based on the problem are determined.
2. Pattern Identification: Once data is explored, refined and defined for the specific variables the
second step is to form pattern identification. Identify and choose the patterns which make the best
prediction.
3.
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Developing Efficient Framework For Social Security Data...
Developing Efficient framework for social Security Data Mining Methodology Ms.Pranjali Barde
Ms.Minal Bobade UG Scholar, JCOET UG Scholar, JCOET Yavatmal, India Yavatmal, India
prajubarde@gmail.com minal02bobade@gmail.com Ms.Rani K. Kakde Ms.Vaishali V Rathod UG
Scholar, JCOET UG Scholar, JCOET Yavatmal, India Yavatmal, India ranikakade87@gmail.com
vaishalirathod155@gmail.com
Abstract– The importance of security for social sites is incredibly important currently days. Typical
welfare countries, like Australia have accumulated an outsized quantity of social insurance and
social welfare knowledge. social insurance data processing is predicated on connected references
from past history on large info of social sites. This includes SSDM framework and problems social
insurance challenges goals in mining the social insurance or welfare knowledge. in this antecedently
work done on techniques for social insurance data processing.
During this paper the term use generate little psychosis for increasing the performance. The
performance of trained little psychosis is rechecked will improve performance by combining the
little psychosis..
Keywords– Keywords are your own designated keywords which can be used for easy location of the
manuscript using any search engines.
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Hightower Department Stores: Imported Stuffed Animals Essay
Hightower Department Stores: Imported Stuffed Animals
Executive Summary
On the morning of January 17, 1993, before the annual buying trip to Germany for the 1993
Christmas season, the toy buyer for the chain of Hightower Department Stores named Julia Brown
was reviewing the performance of some models of stuffed animals tested for sales during 1992.
Every time Julia's on the trip, she would buy some stuffed animals for testing. Fifty was the
minimum amount the manufacturers require. Based on Julia's years of buying experience, the tested
result would give Julia a clear estimation about how many new stuffed animals she needed to order.
Figure 1 in below shows the timeline of how Julia buys the toys for the company: 1992 ... Show
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The imported stuffed animals were just in accordance with Julia's strategies.
The strategies had given the company an advantage that about half of its toys are imported while
other mass merchants and toy supermarkets only had less than 20 percent of imported toys.
Possible Decision Alternatives and Evaluations
According to the decision problem session, three simple decision trees could be developed:
Julia's alternatives are either buying toys from domestic manufacturers, or importing from foreign
countries. Buying from domestic could at least gave $1150 contribution while the contribution of
selling imported toys depended on each different results from test sales. The result of tested sales
seemed to be a good indicator of realized sales, thus linear regression was calculated according to
the test sales and realized sales (excluding those which were not adopted):
Year
Animal
Landed Cost ($)
Retail Price ($)
Sales Projection
Test Sales
Realized Sales
1981
Ape
2.33
4.95
260
27
304
1982
Bear
3.15
5.95
280
19
374
1982
Dragon
2.52
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Diffculty Level Question Questions
Question ID Diffculty Level Question Stimulus Option No.1 Option No.2 Option No.3 Option No.4
Correct Option Subject SUB TOPIC Standard Level Question Reference Solution Methodology
Checked and verified for accuracy (Y/N)
1 2 Decision trees are considered under which class of Machine Learning Algorithms? Supervised
Learning Unsupervised Learning Reinforcement learning Option 1 and 2 both Supervised Learning
Supervised Learning Tree based Modelling
2 3 Decision tree is a type of supervised learning algorithm (having a pre–defined target variable).
Decision Trees can be used for _________ . Classification problems Regression problems Option 1
and 2 both None of these Option 1 and 2 both Supervised Learning Tree based Modelling
3 ... Show more content on Helpwriting.net ...
Disks Squares Circles Triangles Triangles Supervised Learning Tree based Modelling
9 3 Following are the advantage/s of Decision Trees. Choose that apply. "Use a white box model, If
given result is provided by a model" "Worst, best and expected values can be determined for
different scenarios" Possible Scenarios can be added All of the mentioned All of the mentioned
Supervised Learning Tree based Modelling
10 3 Trees based algorithm can be used with which type of Target variable/Dependent
variable/Response variable in the dataset? Categorical variable (e.g YES or NO type) Continuous
Variable Option 1 and 2 both None of these Option 1 and 2 both Supervised Learning Tree based
Modelling
11 3 Leaf/ Terminal Node is represented by ? A Node that do not split further It represents entire
population or sample and this further gets divided into two or more homogeneous sets. A sub–node
that splits into further sub–nodes None of these A Node that do not split further Supervised Learning
Tree based Modelling
12 3 The Leaf nodes of a model tree are? Average of numeric output attribute values. Non–linear
regression equation. linear regression equation. Sum of numeric output attributes values. linear
regression equation. Supervised Learning Tree based Modelling
13 3 What is the process of splitting the tree? A process of dividing a node into two or more sub–
nodes A process of removing sub–nodes of a decision node A process of dividing a
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Smithline Beecham Decision Making
IDEAS AT WORK
By tackling the soft issues such as information quality, credibility, and trust – SB improved its
ability to address the hard ones: how much and where to invest
HOW SMITHKLINE BEECHAM MAKES BETTER RESOURCE–ALLOCATION DECISIONS
BY PAUL SHARPE AND TOM KEELIN greatest, the demands for funding were growing. SB's
executives felt an acute need to rationalize their portlifehlood of any pharmaceuticals folio of
development projects. The company. Ever since the 1989 merger patent on its hlockbuster drug
Tagathat created the company, however, met was about to expire, and the SB believed that it had
been spendcompany was preparing for the iming too much time arguing about pending squeeze: it
had to meet curhow to value its R&JD ... Show more content on Helpwriting.net ...
Their project had always been regarded as a star and had received a lot of attention from
management. They helieved they already had the best plan for the compound's development. They
agreed, however, to look at the other alternatives during a brainstorming session. Several new ideas
emerged. Under the buy–down alternative, the company would drop one of the product forms (oral)
in one of the markets (tumor type B), saving $2 million. Under the buy–up alternative, the company
would increase its investment by $5 million in order to treat a third tumor type (C) with the
intravenous form. When the value of those alternatives was later quantiMarch–April 1998
process evolved into a more sophisticated scoring system based on a project's multiple attributes,
such as commercial potential, technical risk, and investment requirements. Although the approach
looked good on the surface, many people involved in it felt in the end that the company was
following a kind of pseudoscience that lent an air of so–
it is. But solving the organizational problem alone is just as bad. Open discussion may lead to
agreement, enabling a company to move forward. But without a technically sound compass, will it
be moving in the right direction? The easy part of our task was agreeing on the ultimate goal. In our
case, it was to increase shareholder value. The hard part was devising a process that would be
credible to all SB needed a
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Decision Tree
Decision Tree Analysis
Choosing Between Options by Projecting Likely Outcomes
Decision Trees are useful tools for helping you to choose between several courses of action.
They provide a highly effective structure within which you can explore options, and investigate the
possible outcomes of choosing those options. They also help you to form a balanced picture of the
risks and rewards associated with each possible course of action. This makes them particularly
useful for choosing between different strategies, projects or investment opportunities, particularly
when your resources are limited.
How to Use the Tool
You start a Decision Tree with a decision that you need to make. Draw a small square to represent
this on the left hand side ... Show more content on Helpwriting.net ...
Calculating the Value of Decision Nodes
When you are evaluating a decision node, write down the cost of each option along each decision
line. Then subtract the cost from the outcome value that you have already calculated. This will give
you a value that represents the benefit of that decision. Note that amounts already spent do not count
for this analysis – these are 'sunk costs ' and (despite the emotional cost) should not be factored into
the decision.
When you have calculated these decision benefits, choose the option that has the largest benefit, and
take that as the decision made. This is the value of that decision node. Figure 4 shows this
calculation of decision nodes in our example:
In this example, the benefit we previously calculated for 'new product, thorough development ' was
$420,400. We estimate the future cost of this approach as $150,000. This gives a net benefit of
$270,400.
The net benefit of 'new product, rapid development ' was $31,400. On this branch we therefore
choose the most valuable option, 'new product, thorough development ', and allocate this value to
the decision node.
Result By applying this technique we can see that the best option is to develop a new product. It is
worth much more to us to take our time and get the product right, than to rush the product to market.
And it 's better just to improve our existing products than to
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Comparative Analysis Of Data Mining Tools
Comparative Analysis of Data Mining Tools
Research Paper
11/16/2015
Dr. Kweku–Muata Osei–Bryson
1. Executive Summary
This research paper is about the Comparative analysis of three data mining software's selected based
on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. "Data
Mining is a field of study that is gaining importance and is used to explore data in search of patterns
or relationships between variables and is applied to new data used for predictions". (Statistics –
Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is
critical to any research or business and this could impact the business in terms of money, resources
and time. Data experts ... Show more content on Helpwriting.net ...
Hence, different tools have to be used in different scenarios. Each tool varies according to the
environment and the problem type and its nature. A comprehensive framework has been used to
select the best tool and it employs research and findings through numerous questions about each
tool. Each tool is then evaluated based on the criteria and assigned a rank and an overall score is
calculated thus providing results on the credibility of the tools. Hence, we get to identify the strength
and weakness of each tool in this research and finalize the weighted average of these tools. A sample
case study too has been shown using the same framework to find the best tool. The author of the
research paper believes that this framework could help in identifying and selecting the best tools
based on the given criteria. A Likert scale of 1 to 5 has been used in this framework to rank the tools
based on their functioning. Hence, selecting the right software helps in better decision making and
also helps businesses sustain in terms of its resources.
2. Table of Contents
1. Executive Summary 2
3. Introduction 4
3.1 Objectives of the paper 4
3.2 List of Three Decision tree induction software 5
3.3 Limitations of the paper 6
4. Overview on DT induction 7
5. Evaluation criteria 8
5.1 Set of criteria 8
5.2 Definition for each criterion 9
6. Description of the DT induction software 10
6.1 DT
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Decision Tree Analysis On Decision Trees
Decision Tree Analysis
A decision tree is a widespread technique of designing and envisaging predictive patterns and
systems. It is a tree–structured design of a set of aspects to test in direction to expect the output.
Decision trees are effective and accepted implements for prediction and classification. The value of
decision trees is because of the reality that, in compare to neural networks, it signifies rules. Rules
can quickly be articulated so that individuals can comprehend them or even directly use up in a
database retrieve language as structured query language (SQL) so that keep information falling into
a certain sort may be accessed. Decision tree technique is mostly used for data classification, and it
differed into 2 phases; the tree pruning and the tree structure. The training data to create a test
function, conferring to various classification centered on decision tree classification process in
contrast with another, it is a faster, more straightforward and easy to comprehend classification
systems, simply transformed into database uncertainties benefits, and particularly in problem
matters of high dimension can be incredibly decent classification outcomes. The decision tree is a
classification paradigm, applied to remaining data. If we apply it to special data, for which the class
is unidentified, we also get a prognostication of the class. The hypothesis is that the special data
originates from the analogous dissemination as the data we get through to create
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Analysis Of The Book ' Cristobal Colon '
Cristobal Colon who is formerly known as Christopher Columbus was another person that David
Ponder encountered on his journey. Christopher Columbus gave David a very important decision for
success. This decision no longer made David have an undecided heart because David knew that if he
did, he would always fail in life. Thus, the fourth decision for success that was given to David was
"I have a decided heat." When a person like David started "to wait, to wonder, to doubt, to be
indecisive, [they were disobeying] God" (Andrews). When David opted to have a decided heart, he
chose to quit and defeat double mindedness. When people commit themselves to have a decided
heart, they have the power to take hold of their own future. As David read ... Show more content on
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Sometimes in life people try to dehumanize and humiliate others. This causes the humiliated and
dehumanized person to find it hard to forgive others. David found himself struggling with this
concept when he met Abraham Lincoln. Abraham Lincoln gave David excellent advice, which lead
David to the sixth decision for success "I will greet this day with a forgiving spirit." Through this
decision David learned that he must forgive others with grace and mercy. Moreover, that he could
not move on with his life unless he learned to forgive all things. Not only did David learn to forgive
others in this decision for determining personal success, but he also learned to forgive his own self.
When people forgives something, they are doing it for themselves and not for others. Thus, when
David decided to forgive his own self and others, he overcame his feelings of animosity, resentment,
and vengeance. In doing so, the reader could gradually see David becoming a better and more
successful person. In this decision David also choose not to be a slave anymore because he has the
power to speak what he believes. He no longer had to "live his life according to other people"
(Andrews). David learned that he could not be successful unless he started to forgive.
Before David's long journey ended with people from the past, he met Angel Gabriel who gave him
the final decision for success. Angel Gabriel allowed David to look into the future to see what he
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The For A Small Growing Business : Data Tech, Inc.
Jeff Styles started a small growing business called Data Tech, Inc. out of his two–car garage. Data
Tech, Inc. is a company that specializes in transferring hard copies of various business documents
onto CDs . The company started out with anywhere from 10,000–30,000 pieces of mail daily, which
proposed a challenge as business continued to flourish. Since he has accumulated an increasing
number of corporate customers with long–term contracts, Styles has realized that his two–car garage
is now an insufficient space to accommodate the newly acquired business. It has become essential
for Styles to expand his facility in order to meet demands. The issue for Styles to decide between
three different factory locations each with different pros and cons. Furthermore, he must decide on
whether to invest in a large facility or a small facility with the possibility of expansion in the future.
Ranging from most important to least, there were several factors Styles took into consideration
when deciding which location was the right fit for Data Tech, Inc.. Some of the influential factors
included proximity to the airport, proximity to postal service, facility with excess capacity, facility
with potential for expansion, close to business community, and a pleasant environment. A location
near the airport is crucially important to Styles due to his need to travel to various customer
locations. Seeing as Data Tech, Inc. receives numerous amounts of packages daily, a location near a
postal
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The Toyota Motor Manufacturing Canada
5A. List the factors your team considers key to the Toyota Motor Manufacturing Canada (TMMC).
The factors that group two considers important to the Toyota Motor Manufacturing Canada will be
using the weighted scoring model which is a technique used to document decisions or solutions for
management to make informed decisions taking into account all available options when it comes to
resource allocation, (Carroll, Farr & Trainor, 2008).
The group determined that the preferred cost of materials, labor, location, production capacity and
brand as the factors it considers important to the company?s success in Canada.
Toyota Motor Manufacturing Canada (TMMC) leadership made a planned decision to establish its
first Toyota operation outside ... Show more content on Helpwriting.net ...
Cost of materials & Labor (4)
Location (2)
Production Capacity (5)
Brand Recognition (4)
Quality (5)
Endogenous Factors
Toyota Motor Manufacturing Canada (TMMC) growth has meant new opportunities for Canadian
suppliers and has helped create new jobs across Canada. For example to support the launch of the
first Lexus built outside Japan, there have been new suppliers, built plants in communities as well as
the expansion of the suppliers base already there before TMMC moved into Canada. Additionally
because of the lower Canadian dollar compared to the US Dollar, Canadian plants have shown two
areas of high performance, which are quality and high productivity with the third point being cost
competiveness in the corporate income tax rate.
Additionally flexibility, which is very important to Toyota?s Total Production (TPS), does not
change volume by changing of plant layout but increases flexibility in overtime working and general
manpower. This flexibility gives employees the ability to work about 48 hours a week if needed to
meet demands of customers, (Smith, 2005).
Exogenous factors
The group determined that there are factors beyond its control such as material cost and labor,
location is contributes to the external factors that affected its weighted benchmarks. In Canada the
auto industry is still one of the highest paying fields and with Toyota?s reputation, they tend to
attract higher talent which
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Decision Tree Model
Instructor's Manual
Chapter 1
1
Chapter 1
I
1.1
Chapter Outline
A Decision Tree Model and Its Analysis The following concepts are introduced through the use of a
simple decision tree example (the Bill Sampras ' summer job decision): Decision tree Decision node
Event node Mutually exclusive and collectively exhaustive set of events Branches and final values
Expected Monetary Value (EMV) Optimal decision strategy Introduction of the folding back or
backward induction procedure for solving a decision tree. Discussion on sensitivity analysis in a
decision tree. Summary of the General Method of Decision Analysis. Another Decision Tree Model
and Its Analysis Detailed formulation, discussion, and solution of the ... Show more content on
Helpwriting.net ...
If the forecast indicates a rainy day, she should cancel the show. If the forecast indicates a sunny
day, she should continue with the show. The EMV of this strategy is $6,200.
Manual to accompany Data, Models & Decisions: The Fundamentals of Management Science by
Bertsimas and Freund. Copyright 2000, South–Western College Publishing. Prepared by Manuel
Nunez, Chapman University.
Instructor's Manual
Chapter 1
4
1.2
(a) See decision tree above. (b) Once Monday 's bid is made, Newtone 's optimal strategy is to
accept the bid if it is a $3,000,000 bid and reject it if the bid is for $2,000,000. If Monday 's bid is
rejected, then accept Tuesday 's bid, regardless of the amount offered. The EMV of this strategy is
$2,600,000.
Manual to accompany Data, Models & Decisions: The Fundamentals of Management Science by
Bertsimas and Freund. Copyright 2000, South–Western College Publishing. Prepared by Manuel
Nunez, Chapman University.
Instructor's Manual
Chapter 1
5
1.3
(a) As shown in the table below, as p decreases James ' optimal decision changes as to take Meditech
's offer. A break–even analysis where we solve the equation 440p – 200(1–p) = 150 reveals that the
break–even probability is p=0.55. In other words, if the probability of successful 3D software is
below 0.55, then it is better for James to accept Meditech 's offer, otherwise continue with the
project.
Probability of Successful 3D Development p
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The Benefits and Drawbacks of a Binary Tree Versus a...
Homework 3
4. Discuss the benefits and drawbacks of a binary tree versus a bushier tree. The structure of binary
is simple than a bushier tree. Each parent node only has two child. It save the storage space. Besides,
binary tree may deeper than bushier tree. The result record of binary may not very refine. 5.
Construct a classification and regression tree to classify salary based on the other variables. Do as
much as you can by hand, before turning to the software. Data: NO. 1 2 3 4 5 6 7 8 9 10 11 Staff
Sales Management Occupation Service Gender Female Male Male Male Female Male Female
Female Male Female Male Age 45 25 33 25 35 26 45 40 30 50 25 Salary $48,000 $25,000 $35,000
$45,000 $65,000 $45,000 $70,000 $50,000 $40,000 $40,000 ... Show more content on
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The right branch has records 1,3,8,9,10. Now we split the right child which has records 1,3,8,9,10.
Candidate Split Left Child Node, tL Right Child Node, tR
1 3 4 5 8 9 11 12
Occupation = Service Occupation = Sales Occupation = Staff Gender = Female Age 45
Values of the Components of the Optimality Measure =(s|t) for each candidate split, for the Split PL
PR P(L=1|tL) P(L=2|tL) P(L=3|tL) P(L=4|tL) P(L=1|tR) P(L=2|tR) P(L=3|tR) P(L=4|tR) 2PLPR
∅(s|t)
each candidate split, for decision node C
1 3 4 5 8 9
0.40 0.60 0.40 0.60 0.20 0.80 0.60 0.40 0.20 0.80 0.40 0.60
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.50 0.50 1.00
0.50 0.50 0.00 0.67 0.00 0.00 0.33 0.50
0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.67 0.67 0.50 1.00 0.50 0.33 0.50 1.00
0.33
0.00 0.00
... Get more on HelpWriting.net ...
Predictive Analytics And The Health Care Industry
Before proceeding to review a range of predictive analytic algorithms, it is important to know how
critical predictive analytics is to the health care industry. The growth rate of US healthcare
expenditures, increasing annually by nearly 5% in real terms over the last decade and a major
contributor to the high national debt levels projected over the next two decades. McKinsey estimates
that Big Data can enable more than $300 billion savings per year in US healthcare, with two–thirds
of that through reductions of around 8% to national healthcare expenditures. Imagine if there were
health care analytics in the middle ages. The black plague could have been avoided saving millions
of lives of people as it would have been easy to single out the ... Show more content on
Helpwriting.net ...
It could consist of patient–related data, data from healthcare devices like monitors and sensors,
hospital records, application data measuring health metrics and everything including social media
posts, webpages, emergency correspondence, research data from genomics to innovative drugs,
advertisement data, newsfeeds and articles in medical journals. As much as there is scope for finding
out patterns among these data, it is not easy to implement predictive analytics in healthcare industry
because of the limitations like hand–written prescriptions, scanned images and medical records
which comprise of unstructured and disintegrated data. Moreover, medical data is involved with
legal and privacy issues. The adoption rate of analytics in healthcare industry is quite slow making it
more challenging. The Why of applying predictive analytics in healthcare: If predictive analytics is
applied extensively to the rapidly growing healthcare industry, limitless advantages can be realized.
Some of the advantages are: 1) Improved real–time decisions about treatment and support, consumer
commitment 2) Effortless revenue management with focus on global as well as local markets 3)
Standardized clinical processes, guidelines and protocols greatly improving operational efficiency 4)
Reduction in fraud claims, security threats greatly helping insurance companies 5) Mining for
unknown variables that determine quality such as "hidden" re–admission factors or finding out
... Get more on HelpWriting.net ...
Koloon Case Study
Abstract
This case study examines the decision making process of the Kowloon Development Company to
the PrecisionTree decision tree software from Palisade. The Kowloon Development Company was
faced with a major decision about their future investments. The General Manager of the Kowloon
Development Company is usually involved in billion dollar investments, accurate decisions are
needed. The company has to make a decision over the decision to purchase a new development
project the total site area being 16,000 square feet. The objective is to use the use the Decision Tree
software from Palisade, to determine the decision for the Kowloon Development Company whether
or not to purchase the property. Decision trees provide a formal structure in ... Show more content
on Helpwriting.net ...
The discounting rates should
accurately reflect the opportunity cost of capital and consequently the systematic risk of the
project. Quite often, determination of the discounting rates, or the "hurdle" rates, has been based
nothing more than intuition. However, "hurdle" rates lead to incorrect investment decisions
because high return projects are by definition more favored than low return ones. The drawback
is that the absolute expected return of a project is very high, but it is still not high enough to
compensate for the high risk that has to be borne. Or conversely, a project may be expected to
generate very modest return, but this return is already higher than its riskiness. In other words,
the expected return of a project must be commensurate with its risk, or more precisely, it's
systematic or market risk. (HKUST/CEIBS, 1998).
Observation
The objective is to use the use the Decision Tree software from Palisade, to determine the decision
for the Kowloon Development Company whether or not to purchase the property. Decision trees
provide a formal structure in which decisions and chance events are linked in sequence from left to
right. Decisions, chance events, and end results are represented by nodes and connected by
branches. The result is a tree structure with the "root" on the
... Get more on HelpWriting.net ...
Recreational Properties
Management Science – Workshop 2: Case Study Recreational Properties
1. 1. Framing the Decision 2. Recreational Properties obtained a package of options to acquire three
parcels that would allow them to develop a ski resort. The company paid €500,000 for the package
of options in June 2001. The options gave the company the right, but not the obligation, to acquire
the three parcels at a (strike) price of €10 million in June 2002. 3. Furthermore, in order to develop
the three parcels into a ski resort, the company needed leases from the European Union Environment
Agency. When the company purchased the options, they expected the leasing agreement before
December 2001. Unfortunately, a group of conservationists had filed a ... Show more content on
Helpwriting.net ...
(see appendix 4) 3. k. 4. l. Proposal for Reasonable Investment: * m. From point 2, we've seen than
securing the lease would allow the expected value to increase by 3.1725 million. This is therefore
the maximum value we're ready to pay based solely on the main expected value. Nevertheless, if the
risk appetite is lower, we could calculate a value that ensures none of the options to have negative
return (see tree in appendix 5). This value is equal to 1.3 million.
5. 8. Sensitivity Analysis 1. n. Strategy Change with Probability Change: * o. A one–way sensitivity
analysis has been done first based on a whatif table and then, thanks to decision tree tools (appendix
6). This first analysis shows that the breakeven point is at 48%, meaning that if Anders is off by a
few percents on his estimate on the result of the lawsuit, not exercising the options becomes the best
choice. Regarding the reputation, the breakeven point occurs at 68% (see appendix 7). We have
some more margins in that case but the conclusions are the same. A two–way analysis (see appendix
8) shows the safest areas. The (50%– 75%) is close to grey area where a small offset in the
probability might change the decision. 2. p. 3. q. Recommendation: * r. Based on here above
comments, if it is not possible to assess more clearly the probability (by securing the lease or
through a market
... Get more on HelpWriting.net ...
Project Management Study Guide Essay
Decision Trees – Chelst Chapter 10 Exercises – Kimberly Matthews
10.1 – Sequential decisions: Present an example of a sequence of two or more decisions followed by
an uncertainty.
Should we open a bakery or a diner?
If we open a bakery, should we sell specialty items, like wedding cakes, or sell a variety of baked
goods?
If we open a diner, should we be open from 6am – 11pm daily or should we be open 24 hours?
10.2 – Information gathering and decisions: Think of a decision scenario where decisions are
interspersed with random events.
Avon wants to invent their own line of long–wearing lipstick (8+ hours). * Other companies already
offer long–wearing lipstick, so will this just saturate the market? * Can Avon come up with ... Show
more content on Helpwriting.net ...
It is sensitive enough that a 5% decrease in the variable cost of the low investment strategy will
cause a shift in the optimal strategy at around $25.90. b) What do you notice with regard to the
slope?
The slope is zero for the high investment strategy because the variable cost of low investment does
not affect it. The slope of the low investment strategy is negative. As the variable cost of the low
investment increases, the expected value decreases.
10.5 a, b, d and F – a) Calculate the net profit of each combination of decision and competitor
action.
Down Home opens a drive–thru with no competition: $84,000
Down Home opens a drive–thru with competition: $44,000
Down Home serves breakfast with no competition: $113,000
Down Home serves breakfast with competition: $33,000 b) What is the best alternative if no
competitor opens nearby? Serving Breakfast.
What is the best alternative if a competitor opens nearby? Open a drive–thru. d) What decision
should the company follow and what is the expected value? He should serve breakfast, because his
expected value will be $85,000. f) Recall that the owner treated the layout redesign the same as
other annual costs. Would the decision change if he considered only 50% of these redesign costs this
year? He should change to doing a drive–thru service because his expected value would increase to
$120,000.
10.6 b and c b) Should Red Hen
... Get more on HelpWriting.net ...
Freemark Abbey Winery Case
Baur Bektemirov
BUSF 36106: Assignment 5
Freemark Abbey Winery
Assume that under no unusual circumstances (no storm), Jaeger sells 1,000 cases of Riesling.
Consider different cases: 1. Jaeger harvests grapes in anticipations of storm. Then the total revenue
will be equal to 12×1000×$2.85 = $34200. 2. Jaeger doesn't harvest and there is no storm with 50%
chance. 2.1. With 40% chance, sugar concentration is 25%, then the total revenue is 12 × 1000 ×
$3.50 = $42000 2.2. With 40% chance, sugar concentration is 20%, then the total revenue is 12 ×
1000 × $3.00 = $36000 2.3. With 20% chance, sugar concentration is below 20%, then the total
revenue is 12 × 1000 × $2.50 = $30000 3. There is storm with 50% chance 3.1. Storm causes
botrytis ... Show more content on Helpwriting.net ...
MicroPharma will receive 75% of sales in the U.S. and only 10% from sales overseas. v. A firm
receives profit for the 10 years after if launches its product in 2003, starting from year 2004. During
first years, the sales are increasing in an arithmetic progression starting from 0 in 2003 and reaching
the peak in 2008. Since all numbers are in constant dollars, the total sales equal to the sum of sales
in each year. If MegaPharma decides not to buy rights or license from MicroPharma, there is 50%
chance of successful Phase 2 and 80% chance of successful Phase 3 (conditional on success of
Phase 2), and 100% of success in FDA Review. Thus, MegaPharma has 40% chances that the the
compound will be approved. If it passes Phase 2, Phase 3, and Review, MegaPharma will spend $52
million. Total MegaPharma's anticipated sales of the compound are equal to 100 + 200 + 300 + 400
+ 500 + 500 + 500 + 500 + 500 + 500 = 4000 million, total revenue is 75% × 4000 = 3000 million,
if MegaPharma is the only supplier. If both MegaPharma and MicroPharma are on the market, the
anticipated revenue is only a half: $1500 million. Total MicroPharma's anticipated sales in the US
are equal to $3660 million, and the revenue is $2745 million; anticipated sales overseas are equal to
$3660 million and the revenue is $366 million, thus, 3111, if MicroPharma is the monopoly and
3111/2 if it has to share the market with
... Get more on HelpWriting.net ...

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International Guidance And Controls

  • 1. International Guidance and Controls International Guidance and Controls Let S be the cost of project lateness including direct and indirect costs. According to the case the direct cost of lateness is $0.8m per month of lateness, and the indirect cost of lateness is the cost of lost reputation. As an example, if the project is two months late and the cost of lost reputation is estimated to be $2m, then S is $3.6m. Let: SW Only = software only; HW Now = expand hardware now; Delay HW = delay hardware decision; OT = project finished on time; Late = project finished late; FP = favorable software progress in the first five months; NP = learning nothing new in the first five months; and UP = unfavorable software progress in the first five months. ... Show more content on Helpwriting.net ... For S ≥ $3.405m, choose HW Now.  Suppose that S is $1.6m. Now the decision tree is what you see in Figure 3. The overall recommendation is: Choose Delay HW. If FP or NP, then choose SW Only; if UP then choose HW Now. The expected cost of the optimal decision is 3.2906. The risk profile is: SW Only Cost (million $) Probability 3 0.8 4.6 0.2 HW Now Cost (million $) Probability 3.5 1 Delay HW Cost (million $) Probability 3 0.744 3.75 0.14 4.6 0.116 Calculation of EVPI: Suppose that S is $1.6m and we want to compute the EVPI (expected value of perfect information) where the information concerns whether the project would be late or not. We need to consider two scenarios: one where no information is available, and the other where perfect information is available. Note that the Delay HW choice can be ignored in this analysis because
  • 2. delaying the hardware decision is akin to collecting some information on whether the project would be finished on time or not. When no information is available the decision tree is as shown in Figure 4. The expected cost is $3.32m. When perfect information is available the decision tree is as shown in Figure 5. The expected cost is $3.1m. With perfect information the expected cost goes down by $3.32 – $3.1m = $0.22m = $220,000. Hence, EVPI = $220,000. We can also calculate EVPI by first calculating the VPI of each piece of perfect information. Note ... Get more on HelpWriting.net ...
  • 3.
  • 4. Decision Tree Induction & Clustering Techniques in Sas... International Journal of Management & Information Systems – Third Quarter 2010 Volume 14, Number 3 Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And IBM Intelligent Miner – A Comparative Analysis Abdullah M. Al Ghoson, Virginia Commonwealth University, USA ABSTRACT Decision tree induction and Clustering are two of the most prevalent data mining techniques used separately or together in many business applications. Most commercial data mining software tools provide these two techniques but few of them satisfy business needs. There are many criteria and factors to choose the most appropriate software for a particular organization. This paper aims to provide a comparative analysis for three ... Show more content on Helpwriting.net ... In this way, decision trees provide accuracy and explanatory models where the decision tree model is able to explain the reason of certain decisions using these decision rules. Decision trees could be used in classification applications that target discrete value outcomes by classifying unclassified data based on a pre–classified dataset, for example, classifying credit card applicants into three classes of risk, which are low, medium or high. Also, decision trees could be used in estimation applications that have continuous outcomes by estimating value based on pre–classified datasets, and in this case the tree is called a regression tree, for example, estimating household income. Moreover, decision trees could be used in prediction applications that have discrete or continuous outcomes by predicting future value same as classification or estimation, for example, predicting credit card loan as good or bad. 2.1 Decision Tree Models Decision tree models are explanatory models, which are English rules so they are easy to evaluate and understand by people. The decision tree model is considered as a chain of rules that classify records in different bins or classes called nodes [1]. Based on the model 's algorithm, every node may have two or more children or have no child, which is called in this case leaf node [1]. Building decision tree models requires partitioning the pre–classified dataset into three parts, ... Get more on HelpWriting.net ...
  • 5.
  • 6. Valuing Project Achieve Evaluation of Financial Information Syllabus (Subject to minor changes) Spring 2012 Prof. Anna Scherbina UC Davis Graduate School of Management Office: 3212 Gallagher Hall Course Focus Tel: 530.754.8076 e–mail: ascherbina@ucdavis.edu We will learn how to use financial information to value firms, projects, and securities in a wide variety of industries, including real estate. The course will be based entirely on the Harvard Business School case studies and will focus on learning techniques of financial analysis, selecting an appropriate valuation model, analyzing the quality of financial data, finding an appropriate discount rate, and forecasting financial variables and cash flows. Corporate Finance course is strongly suggested as ... Show more content on Helpwriting.net ... Topics covered: buy or rent decision, real estate markets, forecasting Session 2: CASE: Health Development Corporation Topics covered: own or lease decision, the use of multiples Session 3: CASE: Toy World, Inc. Topics covered: forecasting, production methods, balance sheet risks Session 4: CASE: Ocean Carriers Topics covered: cash flow forecasting, macro forecasting Session 5: INTERACTIVE LECTURE and AN IN–CLASS EXERCISE (please bring your laptops): Forecasting macro variables using international equity markets data CASE: Kerr–McGee Topics covered: hostile takeovers, real options Session 6: Session 7: CASE: Merck & Company: Evaluating a Drug Licensing Opportunity Topics covered: decision trees, probability trees, sunk costs, real options Session 8: CASE: Valuing Project Achieve Topics covered: subscriber models, DCF valuation, forecasting
  • 7. 4 Session 9: CASE: NetFlix.com, Inc. Topics covered: subscriber models, forecasting Homework assignment due Session 10: INTERACTIVE LECTURE and AN IN–CLASS EXERCISE (please bring your laptops): Asset Bubbles in Equity and Real Estate Markets CASE QUESTIONS: Module 1 Session 1 Case: Stedman Place Case Questions: 1. What is the cost of renting? 2. What is the cost of buying? 3. Identify the key assumptions and key unknowns that influence the buy–versus–rent decision. ... Get more on HelpWriting.net ...
  • 8.
  • 9. Risk Management Essay RISK MANAGEMENT ESSAY The following essay has been written by analyzing the risks associated from the construction managers/ project managers' point of view. Citing the possible risks associated while working on international or varied geographical location. Risks are associated with almost all levels of the project life cycle and is mutually shared and mitigated by all parties employed within the construction industry. There are many evidences to state that poor risk mitigation leads to poor performance and hence establish risk management processes and practices are required to be adhered to in order to turn any project's outcome into a success. The 2000 edition of the Guide to the Project Management Body of Knowledge (PMI,2000) states ... Show more content on Helpwriting.net ... New entrants who set up similar projects may have access to long term loans at the prevailing rate of interest which may be cheaper. In such a situation, projects that were implemented with high cost borrowings will find it difficult to compete with the new entrants. On the other hand if the interest rate increases in the future, the interest on working capital finance (which normally carries a floating interest rate) increases which will result in lower profit margins than estimated at the time of project appraisal. interest rate risks can be managed to some extent by entering into interest rate hedging agreements like 'interest cap', 'interest swap' etc. 13 | P a g e  EXCHANGE RATE RISK Exchange rate risk, also called as 'currency risk' is the risk arising from currency fluctuations. Volatile exchange rates can reduce cost and productivity advantages gained over years of hard work. Firms exposed to international economy face this risk. When a firm has already committed to a foreign currency denominated transaction, the firm is exposed to a exchange rate risk. The firm will incur a ... Get more on HelpWriting.net ...
  • 10.
  • 11. Data Mining is a Technique Used to Clarify and Classify Data Data Mining is a technique used in various domains to give meaning to the available data and different types of Data to be handled like numerical data, non–numeric data, image data...etc. In classification tree modelling the data is classified to make predictions about new data. Using old data to predict new data has the danger of being too fitted on the old data. In this we evaluated different types of data to be collected from UCI repository for classify the data using the different classification algorithms J48, Naive Bayes, Decision Tree, IBK. This paper evaluates the classification accuracy before applying the feature selection algorithms and comparing the classification accuracy after applying the feature selection with learning algorithms. 1. Introduction As computer and database technologies develop rapidly, data accumulates in a speed unmatchable by human capacity of data processing[2]. Data mining as a multidisciplinary joint effort from databases, machine learning and statistics, is championing in turning mountains of data into nuggets. Researchers and practitioners realize that in order to use data mining tools effectively, data processing is essential to successful data mining.PrimitiveThese are features which have an influence on the output and their role cannot be assumed by the rest.[1] Feature selection can be found in many areas of data mining such as classification, clustering, association rules and regression. For example, feature selection is ... Get more on HelpWriting.net ...
  • 12.
  • 13. Supplier Selection Introduction The evaluation and selection of suppliers, structuring the supplier base is an important task in any organization. It assumes utmost importance in the current scenario of global purchasing. Every Organization especially manufacturing organizations need to have a Supplier evaluation matrix or model in place. This paper tries to bring in a typical Supplier Evaluation Framework, which blends with company's basic values, and help in establishing a Strategic sourcing policy. It also outlines ways and means to reward a supplier and establish long– standing relationships with suppliers. Vendor selection range of criterions Today's consumers demand cheaper, high quality products, on–time delivery and excellent after–sale ... Show more content on Helpwriting.net ... * Ability to meet current and potential capacity requirements, and do so on the desired delivery schedule. * Financial stability. * Technical support availability and willingness to participate as a partner in developing and optimizing design and a long–term relationship. * Total cost of dealing with the supplier (including material cost, communications methods, inventory requirements and incoming verification required). * The supplier 's track record for business–performance improvement. * Total cost assessment. Now when the agreement on the business and vendor requirements had been compiled, the team now must start to search for possible vendors that will be able to deliver the material, product or service. The larger the scope of the vendor selection process the more vendors you should put on the table. Of course, not all vendors will meet the minimum requirements and the team will have to decide which vendors you will seek more information from. Vendor evaluation range of criterions The areas that company chooses to measure and manage and the criteria used will be a direct result of the company 's goals and strategy and the objectives for the supplier performance management program. There are a wide variety of areas of supplier performance that may be measured. It is important to select the ones that are ... Get more on HelpWriting.net ...
  • 14.
  • 15. The Adversarial Risk Analysis Approach Source: Figure 3 (Rios and Insua, 2012) Source: Figure 4 (Rios and Insua, 2012) Source: Figure 5 (Rios and Insua, 2012) Source: Figure 6 (Rios and Insua, 2012) The Adversarial Risk Analysis Approach relaxes the common knowledge assumption in order to make this model more realistic. If the Defender's decision problem is a standard decision analysis problem, shown in Figure 3, with the Attacker's decision node regarded as a random variable. Then her decision tree in Figure 4 illustrates the uncertainty about the Attacker's decision by replacing A (in a square, Fig 3) with A (in a circle, Fig 3). (Rios and Insua 2012) Once the Defender has already assessed pD(S | d, a, v) and uD(d, s, v), she needs pD(A | d), which is ... Show more content on Helpwriting.net ... The Defender's decision is illustrated as a random variable as it is not under control in the Attacker's analysis. The arrow from D (in a circle, Fig 5) to A (in a square, Fig 5) in the influence diagram demonstrates that he will know the Defender's decision while he has to decide. The Defender's private information v, is not known by the Attacker, therefore his uncertainty is demonstrated through a probability distribution pA(V), illustrating the Attacker's previous beliefs about the Defender's private information. Assuming the Defender analyses the Attacker's decision, knowing that he is an expected utility maximiser and uses Bayes's rule to discover about the Defender's private information from monitoring of her defence decision. Consequently, the arrow in the influence diagram from V (in a circle, Fig 5) to D (in a circle, Fig 5), represents probabilistic dependence, is to be inverted to acquire the Attacker's subsequent beliefs about v: pA(V|D=d), yet to acquire this it is needed to assess pA(D|v). (Rios and Insua 2012) If the Defender knew the Attacker's utility function uA(a,s,v) and the probabilities pA(S|d,a,v) and pA(V|d), she could predict his decision a*(d) for any d ∈ D by solving backwards the tree in Figure 6, followed by computing his expected utility ψA. – Compute at chance node S: ψA(d,a,v) for each (d,a,v) as in Equation (2). – Compute for ... Get more on HelpWriting.net ...
  • 16.
  • 17. The Decision Tree Algorithms And Grows The Tree By... This is a pedagogical algorithm, which extracts the rules in the form of decision trees. This is similar to most of the decision tree algorithms and grows the tree by recursive partitioning. At every step it stores a queue of leaves that can be further expanded to sub trees and this process is repeated until a stopping condition is met. Traditional decision trees methods have a limited number of training observations. So they only have fewer number of observations to decide upon the split and leaf node class labels but on the other hand, Trepan re–labels the original observations to the classifications made by the network. And the relabeled data will be used for the tree growing process. Additionally it can also add extra data points by mimicking the behavior of the network. It uses the network as an oracle to answer the classification queries about the newly generated data points. This way it can make sure that whenever a split node or leaf node class decision is made there are at least S_(min ) number of data points. Where S_(min )is a user specified number. Whenever we generate new data points at any particular node, we have to make sure that they satisfy all the constraints from root to the current node. One of the approach to distribute the data points over a network is to employ uniform distribution, but Trepan takes into account the distribution of data i.e. at each node it estimates the marginal distribution of data. If the data at the input is continuous then it ... Get more on HelpWriting.net ...
  • 18.
  • 19. The Cost Effectiveness Of A Drug Or Treatment Rising healthcare costs are a growing concern among individuals, employers, and the federal government. The national conversation on how to best control those costs has forced many drug manufacturers to reevaluate the economics of new, expensive drugs and therapies. Now more than ever, the need to evaluate outcomes and costs associated with alternative treatments has never been greater. Understanding the cost effectiveness of a drug or treatment can be a challenge. Clinical trials are traditionally performed on subsets of the population in tightly controlled environments for a relatively short time. They are primarily responsible for evaluating treatment efficacy. But pressure to control healthcare costs has increased the emphasis on ... Show more content on Helpwriting.net ... Chance nodes (circles) depict the possible consequence – positive or negative – of the decision. They are referred to as transition states. Transition probabilities are assigned to each transition state and they must always sum to one. Triangles indicate the point at which the analysis ends and the health impact and/or costs of each consequence is quantified. When decision tree analysis is done at the same time as the clinical trial, the payoff may also be expressed as utilities. Utility can be described in numerous ways. For example, as a percentage of full health. A value of 0.7 corresponds to a person living at 70% of full health. Another way to express utility is quality adjusted life years (QALY). Expected value of each therapy is calculated by multiplying the payoff (dollars, percent, QALYS etc.) with the probability of occurrence for every possible transition state. While decision trees are simple to comprehend, complicated real–world scenarios cannot be adequately modeled with basic decision tree analysis. The tree cannot model repetitive events or transitions back and forth between two states. To model repetitive events or transitions backward would require numerous repetitive transition states. Trying to create a path for every possible scenario can quickly lead to a complicated, unmanageable decision tree. Another inherent limitation of decision tree analysis is its stagnant nature. Model conditions, such as transition probabilities or costs, are not ... Get more on HelpWriting.net ...
  • 20.
  • 21. What Is Offline And Online Scale Computation? 3.1 Offline and online scale computation For different values of κ the multiscale basis changes as the pde solution changes. To address this issue offline–online computation is used where in offline stage for representative values over a grid of κ's the offline basis and the corresponding linear space is constructed. The online basis then constructed solving a local problem for each candidate κ∗, using the principal component direction of the spectral problem. The mathematical details of the online–offline technique can be found in Guha and Tan (2016), Efendiev et al( 2011). For a set of values κ1,...,κN solving local eigen value problem to detect the dominant scale (inverse of the eigen value) a offline space is constructed with basis ... Show more content on Helpwriting.net ... In particular κ ∼ GP(K) and log(κ(x)) = ‫ﰅ‬ξ √λ e (x), for x ∈ D where K is covariance kernel on D with iii eigen values λi and eigen vectors ei(x) and ξi ∼ N(0,1). 13 The goal is to derive the posterior distribution of κ's or equivalently ξi's given observation and the model. Posterior for κ The posterior for κ can be written as Π(κ|Y ) = p(Y |κ)π(κ) where p(Y |K) is the likelihood of Y given κ which is a normal distribution for the above error distribution assumption around the solution K(u,κ) from the pde. This posterior is not closed form and Metropolis–Hastings MCMC sampling is needed to draw sample from κ. The likelihood computation involves solving for κ and this solution would be calculated using basis function (multiscale basis for example). Using simple coarse scale basis function that has a bilinear form in the coarse grid and zero outside the coarse region, gives a low resolution solution that can be used to filter out the bad proposal value in MCMC set up. 3.3 Multilevel sampling with residue information Selecting the additional basis with this model based distribution the randomized forward solution can be incorporated into a multilevel MCMC methodology (Dodwell et al., 2014). Two level sampling is proposed for convenience but additional levels can be used. Two level sampling are done on the coarse level and fine scale; in coarse scale using coarse scale basis and in the fine level using multiscale basis. ... Get more on HelpWriting.net ...
  • 22.
  • 23. The Probability Of Wining In The Tic-Tac-Toe Game The Goal My goal is to find the probability of wining in tic–tac–toe game given that you make the first move. To obtain hypothesis bases on my goal I have to state some conditions and facts on the game. They are: 1) There are 362, 880 ways of placing O's and X's. 2) When X make first move, possibility of X winning is 131,184, O winning is 77, 904, and 46, 080 tied games (Source: http://en.wikipedia.org/wiki/Tic–tac–toe). After eliminating rotations and/or reflections of other outcomes, there are only 138 unique outcomes. X won 91 times, O won 44 times and 3 ties (Source: http://en.wikipedia.org/wiki/Tic–tac–toe). Basically, the win of X is the concept. There are 8 possible ways of creating three X in row. Based on this my hypothesis ... Show more content on Helpwriting.net ... After that I performed ESX supervised learning with all data as training data as shown below: Then I performed another mining session with only first 658 instances as training data and other 300 as testing data in order to evaluate the model. The parameter window is shown below. Apart from supervised learning, several other mining techniques are used to evaluate the data. They are: Mangrove, a freeware which generates decision tree and classification tree using excel file. All of them will be described more in detail under the evaluating the results division. Interpretation of the results (from Data Mining) When talking about interpreting the results, first we have to check whether the formed classes are solid one. To do this we have to check whether the class resemblance score which should be higher than the domain resemblance score. In this case both Positive and Negative class has score higher than the domain resemblance score. They are only slightly higher because of the nature of the values of the attribute. The scores are shown below. When we look at the domain statistics for the categorical attributes we can see the predictability score for all the attribute value X is more than the predictability score of other two possible values of O (other player move) and b (blank). The scores are shown below. When we examine the class individually I found that the attribute value highly sufficient for being class membership is M–M = X, and the attribute ... Get more on HelpWriting.net ...
  • 24.
  • 25. Decision Analysis Study Decision Analysis Study Decision Analysis Study Introduction This paper will Be providinG a memo that includes many Tasks related To project planning and operations management. All memos are present accordingly to the separated tasks discussed. We will be using the case study of "Shuzworld". As the operations consultant for Shuzworld, we will be following all the tasks and then will provide Recommendations by analyzing the problems given in the task prompts. We will also apply the appropriate decision analysis tool to make reliable and valid Recommendations. Task 4 Part A In this task we will be providing whether Shuzworld should build the proposed stand–alone store, the strip mall store, or not proceed with construction, by ... Show more content on Helpwriting.net ... The profitability problem is one of the most important problems of future profitability when opening a new store process and is the important content of the course of operation management. By recommending the use of this crucial tool, we need to understand, that this will give Shuzworld a direction, to which one of the three options is the best (the stand–alone option, opening of a brand new store or to do nothing now) for the most cost–effectively and profitability option. Also I am recommending this tool, because the decision tree analysis process, which are of great importance in the industrial production of high quantity standardized products (Shoes, sandals, etc) and lately has gained importance in the low volume production of customized products. Due to the high capital requirements when looking for the opening of a new store, decision tree analysis is of great relevance for the manufacturing business. Because of it, this attracted the attention of the manufacturing industry, who tried to support practical cost–effectively analysis by using the proper profitability models. Now, we will analyzed, which one of the three options is the most cost effective and profitable, than the other two. To finally be able to make the proper recommendation. We need to find out, which one of the exact EMV, in which process (option), will become more cost–effective and
  • 26. ... Get more on HelpWriting.net ...
  • 27.
  • 28. Research Assignment: Data Structures and “Space Quest” Research Assignment: Data Structures and "Space Quest" "Space Quest" is a game about a lone traveller, flying through the cosmos. The journey is not a quite one, however, as there are alien Bounty Hunters trying to take down the traveller. The player takes the role of the traveller, and their aim is to avoid an AI controlled alien ship, destined to crash into the player. Unfortunately for the player, it isn't exactly over once the first ship has been outmanoeuvred: there are still other alien spaceships waiting. Luckily for the traveller, the Bounty Hunter's identity is published throughout the galaxy, so devising a strategy will be easy work. For the implementation of 'Space Quest', two data structures were required namely, a Tree, and a ... Show more content on Helpwriting.net ... The enemy AI needs to make decisions based on where the player is located, and attempt to move towards the player. This can only be accomplished through a series of 'Yes and No', or 'True and False', trials. The AI will ask questions such as, "Is the player above me? Is the player below me?" and so forth, therefore, the tree will need to generate and decide on finding the best path to take to approach the player. In order to achieve this, a data structure known as a Decision Tree is required. According to de Ville & Neville (2013), a decision tree can be defined as "a simple, but powerful form of multiple variable analysis." Decision Trees were first introduced over fifty years ago, and are still being refined today to provide new functionality for dealing with the newer code– development related issues we might encounter (de Ville & Neville, 2013). Since decisions trees are essentially binary trees, they also operate in O(log(n)) time complexities for best and worst case scenarios, and O(n) for space. Decision trees are generated by algorithms which determine possible ways to branch–off data depending on the end result of a set question. From this, the aim of the tree would be to predict the probability of a specific outcome. In terms of Space Quest: where will the player be next, and how can they be reached? For each node in the implementation, the AI will ask itself if the defined condition mentioned earlier yielded either a true or false ... Get more on HelpWriting.net ...
  • 29.
  • 30. Analysis Of Genovus Biotechnologies ( Genovus ) Title Genovus Biotechnologies (Genovus) is an infant biomedical company, which is currently working to procure seed A funding. Each member of the Genovus leadership is working to propel the company into existence while holding other jobs to pay basic life expenses. There is a small budget for information technology tools such as email, calendar, and videoconference. The Genovus leadership team needs to decide what information technology options will streamline and simplify the work for the four–person business. This paper reviews two decision making tools, mind mapping and decision tree analysis, and how these two tools may help the Genovus leadership team determine the toolsets needed at this time. Mind mapping is a brainstorming tool. It enables the tool user to engage with concepts in at a deeper level, thereby encouraging longer lasting learning about a topic (Davies, 2011). When concepts are complex, the mind mapping process allows linking like concepts together in a nonlinear model, assisting the user in uncovering dependencies between concepts. Additionally, the visualization of data facilitates the user's ability to quickly think through multiple ideas spontaneously (Davies, 2011). The process of mind mapping increases the quantity and quality of the ideas, while facilitating critical thinking (Luh, Ma, Hsieh, & Huang, 2012). When performed in a team setting, the visualization, and free flow of thinking bring out a variety of ideas in a group (Luh et al., ... Get more on HelpWriting.net ...
  • 31.
  • 32. Data Mining Techniques And Their Applications Data Mining Techniques and Their Applications Deepika Sattu, 800721246, dsattu@uncc.edu Abstract– Data mining is logical process that is used to extract or "mining" large amount of data in order to find useful data [2]. Knowledge discovery from Data or KDD is synonym for Data Mining[13].There are many different types of techniques that can be used to retrieve information from large amount of data. Each type of technique will generate different results. The type of data mining technique that should be selected depends on the type of business problem that we are trying to solve. Keywords: Clustering, Decision Trees, Classification, Prediction I. INTRODUCTION Data is very critical for any organization. In an organization every by year massive amounts of data will be created and how fast your business reacts to that important information determines whether you succeed or fail. The big problem is how we efficiently handle the 3 V's of Big Data. 3V's of Big Data are Volume, Velocity, and Variety. Volume: Amount of Data Velocity: Speed at which the data is being processed. Variety: Usage of data in various forms. i.e., graph, tree, nodes. [14] Now a day's data is in Exa Bytes and Zeta Bytes. It is impossible to manually analyze and extract data. In some Clusters data is increasing where as in others it is decreasing. There are various Data Mining techniques such as Association, Clustering, and Prediction that is used to retrieve data from Large databases (Big ... Get more on HelpWriting.net ...
  • 33.
  • 34. Avalanche Corporation Avalanche Corporation Decision Analysis and Strategic Recommendation Table of Contents Table of Contents 1 Overview 2 Question 1: Production Strategy 2 Question 2: Sensitivity Analysis 3 Question 3: Influence of Outside Vendor 5 Question 4: Alternative Risk Profiles 6 Question 5: Are Fantastic Forecasters Worth It? 7 Conclusions 7 Appendix 8 Figure A: Precision Tree (Question 1) 8 Figure B: Cost Calculation Table 9 Figure C: Profit Calculation Table 9 Figure D: Tornado Graph 10 Figure E: Tornado Graph Data 11 Figure F: Spider Graph 12 Figure G: Decision Tree (No Outsourcing Available) 13 Figure H: Sensitivity of Decision Tree (with Data) 14 Figure I: Strategy Region (with Data) 15 Figure J: ... Show more content on Helpwriting.net ... Precision Tree calculated the EMV for medium and high production which resulted in an outcome of $1,025,000 and $850,000 respectively. Therefore the decision with the highest predicted payoff would be to choose the batch flow and to produce 15,000 items. Question 2: Sensitivity Analysis In order to get a better understanding of what the key inputs or most sensitive input variables were, a sensitivity analysis was conducted on all of the inputs involved in this case. For the sensitivity analysis an arbitrary number of 25% was chosen to vary the inputs in order to analyze their effects on the EMV. For the sake of clarity and simplicity we then decided to focus on several of the main inputs (and combinations of these inputs) that had the greatest effect on the EMV or were deemed necessary for analysis. These were: * Sales Price * Quantity of High or low Demand * Probability of High Demand * Outsource Cost * Clearance Cost As we can see from the tornado graph [Figures D & E] the EMV of the decision tree was most sensitive to the variations in the sales price of the avalanche racer. An increase/decrease of 25% of the sales price of the Avalanche Racer led to an approximate increase/decrease of the EMV by 90% which is statistically significant. This was further supported by the spider graph [Figure F], in which the sales price was also shown as having the ... Get more on HelpWriting.net ...
  • 35.
  • 36. Decision Tree Case Study Decision tree analysis Decision tree analysis known as an analytical tool applied to decision–making under condition of uncertainty, also clarifying where there are many possible outcomes for various alternatives and some outcomes are dependent on previous outcomes. However, decision tree will present as a diagram by showing the relationship among possible courses of action, possible events and the potential outcomes for each course of action in the decision (Drury, 2012). So decision tree analysis is useful for merchant navy company to understand in what direction their chance events are and what their values in terms of profits and losses are for each of the two tooling alternatives, also visualize the outcomes of different prospects ... Show more content on Helpwriting.net ... The marketing management thinks that market shipping new service nationally successful that the expected profits (excluding the cost of the market study) will be ¥1,600,000 however if the market shipping new service nationally failure then it expected loss of ¥700,000(excluding the cost of the market study). However absence of market study, there are equal chances of national success and national failure after if China Shipping (Group) Company decide to market nationally. Therefore now the marketing management has to determine the best strategy that China Shipping (Group) Company should adopt. Excel According to the decision tree above, the optimal decision for China Shipping (Group) Company is to carry out test market and then market nationally if national success then the company will have return in expected profit of ¥654,000 compared to do not conduct test market only have expected profit of ¥450,000. Sensitivity analysis Assuming the probability of national success after the market study will decrease by 15% and the probability of national success without market study will increase by 10%. If a local success is observed, the new probability will decrease from 80% to 65% that new shipping service will be national success. If a local failure is observed, the new probability will decrease from 30% to 15% that new shipping service will be will be national success. However absence of market study and immediately market ... Get more on HelpWriting.net ...
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  • 38. Learning Tree Executive Summary Nowadays, in the education industry has been a highly competitive industry and many new competitors are entering the market. Learning Tree International, Inc. was originated in 1974 and headquartered in Reston, Virginia, it is considered one of the well–known companies in the education industry. According to Yahoo Finance, Learning Tree Inc. has 393 full–time employees. Learning Tree International, Inc. (LTRE), operates in the education and training services industry (SIC code: 8200). The services that the company provides are training and education for commercial and government information technology and management professionals. Also, it known for its spread worldwide and that they offer their services online through what they call "Learning ... Show more content on Helpwriting.net ... The return on equity ratio for the company is –66.01%, K12's is 3.7%, and the industry is 21.35%, This is another indication that the company is not operating well and that the shareholders are currently not earning from their investments in the company. On the other hand, the competitor – K12– is also operating poorly with comparison to the industry's average percentage, but it is performing better than Learning Tree International, Inc. Also, using another profitability ratio, which is return on assets. The company's ratio is –13.3, K12's ratio is 2.74, and the industry's ratio is 11.85; consequently, the company has a negative percentage while the percentages for the industry and K12 are positive. So the company is not employing its total asset to generate profit as the same as K12. In short, when comparing the profitability ratios of the company with industry and K12, it shows that the company is in unstable condition with its investors. Moreover, the earning per share for Learning Tree International, Inc. over the last three years are: $–0.90 on 9/12, $–0.66 on 9/13, and $–0.50 on 9/14. Even though the company still has a negative EPS, but it has been increasing from year to year. In addition, the price/sale ratio is a ratio that measure the stock price with the annual sales and could be a good comparison between companies. The company's P/S ratio is 0.27, K12 is 0.77, and the industry is 1.3, so we can tell that the company is clearly below its competitor and its ... Get more on HelpWriting.net ...
  • 39.
  • 40. The Decision Tree Method For Intrusion Detection System Abstract There are many risks in using the internet irrespective of its popularity. These risks are network attack, and attack method which vary every day. This research is aim to compare decision tree method for intrusion detection. As intrusion detection is one of the major research problem in network security. Tradition intrusion detection system contain a number of problems, such as low performance, high false negative rate, low intelligent level. In this research work we compared effectiveness of decision tree method in Intrusion Detection System. We also compare the detection rate, false alarm rate for different types of attack. 1.0 Background Intrusion Detection Systems (IDS) are software or hardware designed to automatically monitor activities within a network of computers and identify any security issues . IDS have been around for at least 30 years since increased enterprise network access produced a new challenge, the need for user access and monitoring. As day–to–day operations grew increasingly dependent upon shared use of information systems, levels of access to these systems and clear visibility into user activity was required to operate safely and securely. Many of the initial headway on IDS was made within the U.S. Air Force. In 1980, James P. Anderson, an innovator of information security and member of the Defense Science Board Task Force on Computer Security at the U.S. Air Force, produced "Computer Security Threat Monitoring and Surveillance," a ... Get more on HelpWriting.net ...
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  • 42. A Survey On Data Mining Classification Algorithms A Survey on Data Mining Classification Algorithms Abstract: Classification is one of the most familiar data mining technique and model finding process that is used for transmission the data into different classes according to particular condition. Further the classification is used to forecast group relationship for precise data instance. It is generally construct models that are used to predict potential statistics trends. The major objective of machine data is to perfectly predict the class for each record. This article focuses on a survey on different classification techniques that are mostly used in data–mining. Keywords: Data mining, Classification, decision tree, neural network. 1. INTRODUCTION Data mining is one of the many ... Show more content on Helpwriting.net ... Classification contains finding rules that partition the data into disjoint groups patterns and process. The goal of classification is to evaluate the input data to develop a precise. Explanation or model for each class using the features by using the present data. 2. ARCHITECTURE OF DATA MINING Data mining and knowledge discovery is the name frequently used to refer to a very interdisciplinary field, which consists of using methods of several research areas to extract knowledge from real–world datasets. There is a distinction between the terms data mining and knowledge discovery which seems to have been introduced by [Fayyad et al.1996].the term data mining refers to the core step of a broader process, called knowledge discovery in database. Architecture of data mining structure is defined the following figure. 3. DATA MINING PROCESS  Data cleaning  Data integration  Data selection  Data transformation  Pattern evaluation  Knowledge presentation. Data cleaning: Data cleaning or data scrubbing is the process of detecting as well as correcting (or removing) inaccurate data from a record set. It handles noisy data it represents random error in attribute values. In very large dataset noise can come in many shapes and forms. And irrelevant data handles the missing and unnecessary data in the source file. Data integration: Data integration process contains the data from multiple sources. ... Get more on HelpWriting.net ...
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  • 44. Improving Decision Tree Performance Methods There are several improvement methods are available to improve decision tree performance in terms of accuracy, and modelling time. Since experimenting with every available method is impossible, some of the methods are selected that are proven to increase decision tree performances. Selected improvement methods and their experimental setups are presented in this chapter. 4.1 Correlation–Based Feature Selection Feature selection is a method used for reducing number of dimensions of a dataset by removing irrelevant and redundant attributes. Given a set of attributes F and a target class C, goal of feature selection is to find a minimum set of F that will yield highest accuracy (for C) for the classification task. Although ... Show more content on Helpwriting.net ... Also, method is performing well for C4.5 algorithm is likely to perform well for ID3 algorithm. Previous studies show that CFS method increases accuracy for CART algorithm although not as much as the C4.5 algorithm does (Doraisamy et al., 2008). CFS uses a search algorithm and feature evaluation algorithm which uses a heuristic that measures "goodness" of attributes subsets. Hall and Smith (1998) define this goodness heuristic as "Good feature subsets contain features highly correlated with the class, yet uncorrelated with each other." Equation 1 below shows heuristic formula. G_x=(k¯(r_ci ))/√(k+k(k–1)¯(r_ii ' )) Where G_x is the heuristic of goodness of an attribute subset x that contains k features, ¯(r_ci ) is average attribute–class correlation which points predictive power of the attribute subset to a class, and ¯(r_ii ' ) is average attribute inter–correlation that indicates the redundancy among attributes. A version of correlation–based attribute selection to be included in experiment setup is called Fast Correlation–Based Feature Selection (FCBF) that initially developed by Yu and Liu (2004). This algorithm is preferred over other available correlation–based attribute selection algorithms since while other implementations of CFS using forward–sequential or greedy search methods (e.g. MRMR/CFS developed by Schoewe, ... Get more on HelpWriting.net ...
  • 45.
  • 46. Data Mining Techniques And Their Applications Data Mining Techniques and Their Applications in Financial Data Analysis Deepika Sattu, 800721246, dsattu@uncc.edu Abstract– Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve. Keywords: Clustering, Decision Trees, Classification, Prediction I. INTRODUCTION Data is very critical for any organization, industry or business process. Data which was in gigabytes or terabytes ... Show more content on Helpwriting.net ... Here are the few typical cases: Design and construction of data warehouses for multidimensional data analysis. Loan payment prediction and customer credit policy analysis. Classification and clustering of customers for targeted marketing. Detection of money laundering and other financial crimes.[6] II. DATA MINING TECHNIQUES Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve. Different analyses will deliver different outcomes and thus provide different insights [1]. Below are the three steps involved to make certain decisions for development of their businesses [2] 1. Exploration: In the first step of data exploration data is cleaned and transformed into another form, and important variables and then nature of data based on the problem are determined. 2. Pattern Identification: Once data is explored, refined and defined for the specific variables the
  • 47. second step is to form pattern identification. Identify and choose the patterns which make the best prediction. 3. ... Get more on HelpWriting.net ...
  • 48.
  • 49. Developing Efficient Framework For Social Security Data... Developing Efficient framework for social Security Data Mining Methodology Ms.Pranjali Barde Ms.Minal Bobade UG Scholar, JCOET UG Scholar, JCOET Yavatmal, India Yavatmal, India prajubarde@gmail.com minal02bobade@gmail.com Ms.Rani K. Kakde Ms.Vaishali V Rathod UG Scholar, JCOET UG Scholar, JCOET Yavatmal, India Yavatmal, India ranikakade87@gmail.com vaishalirathod155@gmail.com Abstract– The importance of security for social sites is incredibly important currently days. Typical welfare countries, like Australia have accumulated an outsized quantity of social insurance and social welfare knowledge. social insurance data processing is predicated on connected references from past history on large info of social sites. This includes SSDM framework and problems social insurance challenges goals in mining the social insurance or welfare knowledge. in this antecedently work done on techniques for social insurance data processing. During this paper the term use generate little psychosis for increasing the performance. The performance of trained little psychosis is rechecked will improve performance by combining the little psychosis.. Keywords– Keywords are your own designated keywords which can be used for easy location of the manuscript using any search engines. ... Get more on HelpWriting.net ...
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  • 51. Hightower Department Stores: Imported Stuffed Animals Essay Hightower Department Stores: Imported Stuffed Animals Executive Summary On the morning of January 17, 1993, before the annual buying trip to Germany for the 1993 Christmas season, the toy buyer for the chain of Hightower Department Stores named Julia Brown was reviewing the performance of some models of stuffed animals tested for sales during 1992. Every time Julia's on the trip, she would buy some stuffed animals for testing. Fifty was the minimum amount the manufacturers require. Based on Julia's years of buying experience, the tested result would give Julia a clear estimation about how many new stuffed animals she needed to order. Figure 1 in below shows the timeline of how Julia buys the toys for the company: 1992 ... Show more content on Helpwriting.net ... The imported stuffed animals were just in accordance with Julia's strategies. The strategies had given the company an advantage that about half of its toys are imported while other mass merchants and toy supermarkets only had less than 20 percent of imported toys. Possible Decision Alternatives and Evaluations According to the decision problem session, three simple decision trees could be developed: Julia's alternatives are either buying toys from domestic manufacturers, or importing from foreign countries. Buying from domestic could at least gave $1150 contribution while the contribution of selling imported toys depended on each different results from test sales. The result of tested sales seemed to be a good indicator of realized sales, thus linear regression was calculated according to the test sales and realized sales (excluding those which were not adopted): Year Animal Landed Cost ($) Retail Price ($) Sales Projection Test Sales Realized Sales 1981 Ape 2.33 4.95 260 27 304
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  • 54. Diffculty Level Question Questions Question ID Diffculty Level Question Stimulus Option No.1 Option No.2 Option No.3 Option No.4 Correct Option Subject SUB TOPIC Standard Level Question Reference Solution Methodology Checked and verified for accuracy (Y/N) 1 2 Decision trees are considered under which class of Machine Learning Algorithms? Supervised Learning Unsupervised Learning Reinforcement learning Option 1 and 2 both Supervised Learning Supervised Learning Tree based Modelling 2 3 Decision tree is a type of supervised learning algorithm (having a pre–defined target variable). Decision Trees can be used for _________ . Classification problems Regression problems Option 1 and 2 both None of these Option 1 and 2 both Supervised Learning Tree based Modelling 3 ... Show more content on Helpwriting.net ... Disks Squares Circles Triangles Triangles Supervised Learning Tree based Modelling 9 3 Following are the advantage/s of Decision Trees. Choose that apply. "Use a white box model, If given result is provided by a model" "Worst, best and expected values can be determined for different scenarios" Possible Scenarios can be added All of the mentioned All of the mentioned Supervised Learning Tree based Modelling 10 3 Trees based algorithm can be used with which type of Target variable/Dependent variable/Response variable in the dataset? Categorical variable (e.g YES or NO type) Continuous Variable Option 1 and 2 both None of these Option 1 and 2 both Supervised Learning Tree based Modelling 11 3 Leaf/ Terminal Node is represented by ? A Node that do not split further It represents entire population or sample and this further gets divided into two or more homogeneous sets. A sub–node that splits into further sub–nodes None of these A Node that do not split further Supervised Learning Tree based Modelling 12 3 The Leaf nodes of a model tree are? Average of numeric output attribute values. Non–linear regression equation. linear regression equation. Sum of numeric output attributes values. linear regression equation. Supervised Learning Tree based Modelling 13 3 What is the process of splitting the tree? A process of dividing a node into two or more sub– nodes A process of removing sub–nodes of a decision node A process of dividing a ... Get more on HelpWriting.net ...
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  • 56. Smithline Beecham Decision Making IDEAS AT WORK By tackling the soft issues such as information quality, credibility, and trust – SB improved its ability to address the hard ones: how much and where to invest HOW SMITHKLINE BEECHAM MAKES BETTER RESOURCE–ALLOCATION DECISIONS BY PAUL SHARPE AND TOM KEELIN greatest, the demands for funding were growing. SB's executives felt an acute need to rationalize their portlifehlood of any pharmaceuticals folio of development projects. The company. Ever since the 1989 merger patent on its hlockbuster drug Tagathat created the company, however, met was about to expire, and the SB believed that it had been spendcompany was preparing for the iming too much time arguing about pending squeeze: it had to meet curhow to value its R&JD ... Show more content on Helpwriting.net ... Their project had always been regarded as a star and had received a lot of attention from management. They helieved they already had the best plan for the compound's development. They agreed, however, to look at the other alternatives during a brainstorming session. Several new ideas emerged. Under the buy–down alternative, the company would drop one of the product forms (oral) in one of the markets (tumor type B), saving $2 million. Under the buy–up alternative, the company would increase its investment by $5 million in order to treat a third tumor type (C) with the intravenous form. When the value of those alternatives was later quantiMarch–April 1998 process evolved into a more sophisticated scoring system based on a project's multiple attributes, such as commercial potential, technical risk, and investment requirements. Although the approach looked good on the surface, many people involved in it felt in the end that the company was following a kind of pseudoscience that lent an air of so– it is. But solving the organizational problem alone is just as bad. Open discussion may lead to agreement, enabling a company to move forward. But without a technically sound compass, will it be moving in the right direction? The easy part of our task was agreeing on the ultimate goal. In our case, it was to increase shareholder value. The hard part was devising a process that would be credible to all SB needed a ... Get more on HelpWriting.net ...
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  • 58. Decision Tree Decision Tree Analysis Choosing Between Options by Projecting Likely Outcomes Decision Trees are useful tools for helping you to choose between several courses of action. They provide a highly effective structure within which you can explore options, and investigate the possible outcomes of choosing those options. They also help you to form a balanced picture of the risks and rewards associated with each possible course of action. This makes them particularly useful for choosing between different strategies, projects or investment opportunities, particularly when your resources are limited. How to Use the Tool You start a Decision Tree with a decision that you need to make. Draw a small square to represent this on the left hand side ... Show more content on Helpwriting.net ... Calculating the Value of Decision Nodes When you are evaluating a decision node, write down the cost of each option along each decision line. Then subtract the cost from the outcome value that you have already calculated. This will give you a value that represents the benefit of that decision. Note that amounts already spent do not count for this analysis – these are 'sunk costs ' and (despite the emotional cost) should not be factored into the decision. When you have calculated these decision benefits, choose the option that has the largest benefit, and take that as the decision made. This is the value of that decision node. Figure 4 shows this calculation of decision nodes in our example: In this example, the benefit we previously calculated for 'new product, thorough development ' was $420,400. We estimate the future cost of this approach as $150,000. This gives a net benefit of $270,400. The net benefit of 'new product, rapid development ' was $31,400. On this branch we therefore choose the most valuable option, 'new product, thorough development ', and allocate this value to the decision node. Result By applying this technique we can see that the best option is to develop a new product. It is worth much more to us to take our time and get the product right, than to rush the product to market. And it 's better just to improve our existing products than to ... Get more on HelpWriting.net ...
  • 59.
  • 60. Comparative Analysis Of Data Mining Tools Comparative Analysis of Data Mining Tools Research Paper 11/16/2015 Dr. Kweku–Muata Osei–Bryson 1. Executive Summary This research paper is about the Comparative analysis of three data mining software's selected based on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. "Data Mining is a field of study that is gaining importance and is used to explore data in search of patterns or relationships between variables and is applied to new data used for predictions". (Statistics – Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is critical to any research or business and this could impact the business in terms of money, resources and time. Data experts ... Show more content on Helpwriting.net ... Hence, different tools have to be used in different scenarios. Each tool varies according to the environment and the problem type and its nature. A comprehensive framework has been used to select the best tool and it employs research and findings through numerous questions about each tool. Each tool is then evaluated based on the criteria and assigned a rank and an overall score is calculated thus providing results on the credibility of the tools. Hence, we get to identify the strength and weakness of each tool in this research and finalize the weighted average of these tools. A sample case study too has been shown using the same framework to find the best tool. The author of the research paper believes that this framework could help in identifying and selecting the best tools based on the given criteria. A Likert scale of 1 to 5 has been used in this framework to rank the tools based on their functioning. Hence, selecting the right software helps in better decision making and also helps businesses sustain in terms of its resources. 2. Table of Contents 1. Executive Summary 2 3. Introduction 4 3.1 Objectives of the paper 4 3.2 List of Three Decision tree induction software 5 3.3 Limitations of the paper 6 4. Overview on DT induction 7 5. Evaluation criteria 8 5.1 Set of criteria 8 5.2 Definition for each criterion 9
  • 61. 6. Description of the DT induction software 10 6.1 DT ... Get more on HelpWriting.net ...
  • 62.
  • 63. Decision Tree Analysis On Decision Trees Decision Tree Analysis A decision tree is a widespread technique of designing and envisaging predictive patterns and systems. It is a tree–structured design of a set of aspects to test in direction to expect the output. Decision trees are effective and accepted implements for prediction and classification. The value of decision trees is because of the reality that, in compare to neural networks, it signifies rules. Rules can quickly be articulated so that individuals can comprehend them or even directly use up in a database retrieve language as structured query language (SQL) so that keep information falling into a certain sort may be accessed. Decision tree technique is mostly used for data classification, and it differed into 2 phases; the tree pruning and the tree structure. The training data to create a test function, conferring to various classification centered on decision tree classification process in contrast with another, it is a faster, more straightforward and easy to comprehend classification systems, simply transformed into database uncertainties benefits, and particularly in problem matters of high dimension can be incredibly decent classification outcomes. The decision tree is a classification paradigm, applied to remaining data. If we apply it to special data, for which the class is unidentified, we also get a prognostication of the class. The hypothesis is that the special data originates from the analogous dissemination as the data we get through to create ... Get more on HelpWriting.net ...
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  • 65. Analysis Of The Book ' Cristobal Colon ' Cristobal Colon who is formerly known as Christopher Columbus was another person that David Ponder encountered on his journey. Christopher Columbus gave David a very important decision for success. This decision no longer made David have an undecided heart because David knew that if he did, he would always fail in life. Thus, the fourth decision for success that was given to David was "I have a decided heat." When a person like David started "to wait, to wonder, to doubt, to be indecisive, [they were disobeying] God" (Andrews). When David opted to have a decided heart, he chose to quit and defeat double mindedness. When people commit themselves to have a decided heart, they have the power to take hold of their own future. As David read ... Show more content on Helpwriting.net ... Sometimes in life people try to dehumanize and humiliate others. This causes the humiliated and dehumanized person to find it hard to forgive others. David found himself struggling with this concept when he met Abraham Lincoln. Abraham Lincoln gave David excellent advice, which lead David to the sixth decision for success "I will greet this day with a forgiving spirit." Through this decision David learned that he must forgive others with grace and mercy. Moreover, that he could not move on with his life unless he learned to forgive all things. Not only did David learn to forgive others in this decision for determining personal success, but he also learned to forgive his own self. When people forgives something, they are doing it for themselves and not for others. Thus, when David decided to forgive his own self and others, he overcame his feelings of animosity, resentment, and vengeance. In doing so, the reader could gradually see David becoming a better and more successful person. In this decision David also choose not to be a slave anymore because he has the power to speak what he believes. He no longer had to "live his life according to other people" (Andrews). David learned that he could not be successful unless he started to forgive. Before David's long journey ended with people from the past, he met Angel Gabriel who gave him the final decision for success. Angel Gabriel allowed David to look into the future to see what he ... Get more on HelpWriting.net ...
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  • 67. The For A Small Growing Business : Data Tech, Inc. Jeff Styles started a small growing business called Data Tech, Inc. out of his two–car garage. Data Tech, Inc. is a company that specializes in transferring hard copies of various business documents onto CDs . The company started out with anywhere from 10,000–30,000 pieces of mail daily, which proposed a challenge as business continued to flourish. Since he has accumulated an increasing number of corporate customers with long–term contracts, Styles has realized that his two–car garage is now an insufficient space to accommodate the newly acquired business. It has become essential for Styles to expand his facility in order to meet demands. The issue for Styles to decide between three different factory locations each with different pros and cons. Furthermore, he must decide on whether to invest in a large facility or a small facility with the possibility of expansion in the future. Ranging from most important to least, there were several factors Styles took into consideration when deciding which location was the right fit for Data Tech, Inc.. Some of the influential factors included proximity to the airport, proximity to postal service, facility with excess capacity, facility with potential for expansion, close to business community, and a pleasant environment. A location near the airport is crucially important to Styles due to his need to travel to various customer locations. Seeing as Data Tech, Inc. receives numerous amounts of packages daily, a location near a postal ... Get more on HelpWriting.net ...
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  • 69. The Toyota Motor Manufacturing Canada 5A. List the factors your team considers key to the Toyota Motor Manufacturing Canada (TMMC). The factors that group two considers important to the Toyota Motor Manufacturing Canada will be using the weighted scoring model which is a technique used to document decisions or solutions for management to make informed decisions taking into account all available options when it comes to resource allocation, (Carroll, Farr & Trainor, 2008). The group determined that the preferred cost of materials, labor, location, production capacity and brand as the factors it considers important to the company?s success in Canada. Toyota Motor Manufacturing Canada (TMMC) leadership made a planned decision to establish its first Toyota operation outside ... Show more content on Helpwriting.net ... Cost of materials & Labor (4) Location (2) Production Capacity (5) Brand Recognition (4) Quality (5) Endogenous Factors Toyota Motor Manufacturing Canada (TMMC) growth has meant new opportunities for Canadian suppliers and has helped create new jobs across Canada. For example to support the launch of the first Lexus built outside Japan, there have been new suppliers, built plants in communities as well as the expansion of the suppliers base already there before TMMC moved into Canada. Additionally because of the lower Canadian dollar compared to the US Dollar, Canadian plants have shown two areas of high performance, which are quality and high productivity with the third point being cost competiveness in the corporate income tax rate. Additionally flexibility, which is very important to Toyota?s Total Production (TPS), does not change volume by changing of plant layout but increases flexibility in overtime working and general manpower. This flexibility gives employees the ability to work about 48 hours a week if needed to meet demands of customers, (Smith, 2005). Exogenous factors The group determined that there are factors beyond its control such as material cost and labor, location is contributes to the external factors that affected its weighted benchmarks. In Canada the auto industry is still one of the highest paying fields and with Toyota?s reputation, they tend to attract higher talent which ... Get more on HelpWriting.net ...
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  • 71. Decision Tree Model Instructor's Manual Chapter 1 1 Chapter 1 I 1.1 Chapter Outline A Decision Tree Model and Its Analysis The following concepts are introduced through the use of a simple decision tree example (the Bill Sampras ' summer job decision): Decision tree Decision node Event node Mutually exclusive and collectively exhaustive set of events Branches and final values Expected Monetary Value (EMV) Optimal decision strategy Introduction of the folding back or backward induction procedure for solving a decision tree. Discussion on sensitivity analysis in a decision tree. Summary of the General Method of Decision Analysis. Another Decision Tree Model and Its Analysis Detailed formulation, discussion, and solution of the ... Show more content on Helpwriting.net ... If the forecast indicates a rainy day, she should cancel the show. If the forecast indicates a sunny day, she should continue with the show. The EMV of this strategy is $6,200. Manual to accompany Data, Models & Decisions: The Fundamentals of Management Science by Bertsimas and Freund. Copyright 2000, South–Western College Publishing. Prepared by Manuel Nunez, Chapman University. Instructor's Manual Chapter 1 4 1.2 (a) See decision tree above. (b) Once Monday 's bid is made, Newtone 's optimal strategy is to accept the bid if it is a $3,000,000 bid and reject it if the bid is for $2,000,000. If Monday 's bid is
  • 72. rejected, then accept Tuesday 's bid, regardless of the amount offered. The EMV of this strategy is $2,600,000. Manual to accompany Data, Models & Decisions: The Fundamentals of Management Science by Bertsimas and Freund. Copyright 2000, South–Western College Publishing. Prepared by Manuel Nunez, Chapman University. Instructor's Manual Chapter 1 5 1.3 (a) As shown in the table below, as p decreases James ' optimal decision changes as to take Meditech 's offer. A break–even analysis where we solve the equation 440p – 200(1–p) = 150 reveals that the break–even probability is p=0.55. In other words, if the probability of successful 3D software is below 0.55, then it is better for James to accept Meditech 's offer, otherwise continue with the project. Probability of Successful 3D Development p ... Get more on HelpWriting.net ...
  • 73.
  • 74. The Benefits and Drawbacks of a Binary Tree Versus a... Homework 3 4. Discuss the benefits and drawbacks of a binary tree versus a bushier tree. The structure of binary is simple than a bushier tree. Each parent node only has two child. It save the storage space. Besides, binary tree may deeper than bushier tree. The result record of binary may not very refine. 5. Construct a classification and regression tree to classify salary based on the other variables. Do as much as you can by hand, before turning to the software. Data: NO. 1 2 3 4 5 6 7 8 9 10 11 Staff Sales Management Occupation Service Gender Female Male Male Male Female Male Female Female Male Female Male Age 45 25 33 25 35 26 45 40 30 50 25 Salary $48,000 $25,000 $35,000 $45,000 $65,000 $45,000 $70,000 $50,000 $40,000 $40,000 ... Show more content on Helpwriting.net ... The right branch has records 1,3,8,9,10. Now we split the right child which has records 1,3,8,9,10. Candidate Split Left Child Node, tL Right Child Node, tR 1 3 4 5 8 9 11 12 Occupation = Service Occupation = Sales Occupation = Staff Gender = Female Age 45 Values of the Components of the Optimality Measure =(s|t) for each candidate split, for the Split PL PR P(L=1|tL) P(L=2|tL) P(L=3|tL) P(L=4|tL) P(L=1|tR) P(L=2|tR) P(L=3|tR) P(L=4|tR) 2PLPR ∅(s|t) each candidate split, for decision node C 1 3 4 5 8 9 0.40 0.60 0.40 0.60 0.20 0.80 0.60 0.40 0.20 0.80 0.40 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.50 1.00 0.50 0.50 0.00 0.67 0.00 0.00 0.33 0.50 0.00 0.00 0.00
  • 75. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.67 0.50 1.00 0.50 0.33 0.50 1.00 0.33 0.00 0.00 ... Get more on HelpWriting.net ...
  • 76.
  • 77. Predictive Analytics And The Health Care Industry Before proceeding to review a range of predictive analytic algorithms, it is important to know how critical predictive analytics is to the health care industry. The growth rate of US healthcare expenditures, increasing annually by nearly 5% in real terms over the last decade and a major contributor to the high national debt levels projected over the next two decades. McKinsey estimates that Big Data can enable more than $300 billion savings per year in US healthcare, with two–thirds of that through reductions of around 8% to national healthcare expenditures. Imagine if there were health care analytics in the middle ages. The black plague could have been avoided saving millions of lives of people as it would have been easy to single out the ... Show more content on Helpwriting.net ... It could consist of patient–related data, data from healthcare devices like monitors and sensors, hospital records, application data measuring health metrics and everything including social media posts, webpages, emergency correspondence, research data from genomics to innovative drugs, advertisement data, newsfeeds and articles in medical journals. As much as there is scope for finding out patterns among these data, it is not easy to implement predictive analytics in healthcare industry because of the limitations like hand–written prescriptions, scanned images and medical records which comprise of unstructured and disintegrated data. Moreover, medical data is involved with legal and privacy issues. The adoption rate of analytics in healthcare industry is quite slow making it more challenging. The Why of applying predictive analytics in healthcare: If predictive analytics is applied extensively to the rapidly growing healthcare industry, limitless advantages can be realized. Some of the advantages are: 1) Improved real–time decisions about treatment and support, consumer commitment 2) Effortless revenue management with focus on global as well as local markets 3) Standardized clinical processes, guidelines and protocols greatly improving operational efficiency 4) Reduction in fraud claims, security threats greatly helping insurance companies 5) Mining for unknown variables that determine quality such as "hidden" re–admission factors or finding out ... Get more on HelpWriting.net ...
  • 78.
  • 79. Koloon Case Study Abstract This case study examines the decision making process of the Kowloon Development Company to the PrecisionTree decision tree software from Palisade. The Kowloon Development Company was faced with a major decision about their future investments. The General Manager of the Kowloon Development Company is usually involved in billion dollar investments, accurate decisions are needed. The company has to make a decision over the decision to purchase a new development project the total site area being 16,000 square feet. The objective is to use the use the Decision Tree software from Palisade, to determine the decision for the Kowloon Development Company whether or not to purchase the property. Decision trees provide a formal structure in ... Show more content on Helpwriting.net ... The discounting rates should accurately reflect the opportunity cost of capital and consequently the systematic risk of the project. Quite often, determination of the discounting rates, or the "hurdle" rates, has been based nothing more than intuition. However, "hurdle" rates lead to incorrect investment decisions because high return projects are by definition more favored than low return ones. The drawback is that the absolute expected return of a project is very high, but it is still not high enough to compensate for the high risk that has to be borne. Or conversely, a project may be expected to generate very modest return, but this return is already higher than its riskiness. In other words, the expected return of a project must be commensurate with its risk, or more precisely, it's systematic or market risk. (HKUST/CEIBS, 1998). Observation The objective is to use the use the Decision Tree software from Palisade, to determine the decision for the Kowloon Development Company whether or not to purchase the property. Decision trees provide a formal structure in which decisions and chance events are linked in sequence from left to
  • 80. right. Decisions, chance events, and end results are represented by nodes and connected by branches. The result is a tree structure with the "root" on the ... Get more on HelpWriting.net ...
  • 81.
  • 82. Recreational Properties Management Science – Workshop 2: Case Study Recreational Properties 1. 1. Framing the Decision 2. Recreational Properties obtained a package of options to acquire three parcels that would allow them to develop a ski resort. The company paid €500,000 for the package of options in June 2001. The options gave the company the right, but not the obligation, to acquire the three parcels at a (strike) price of €10 million in June 2002. 3. Furthermore, in order to develop the three parcels into a ski resort, the company needed leases from the European Union Environment Agency. When the company purchased the options, they expected the leasing agreement before December 2001. Unfortunately, a group of conservationists had filed a ... Show more content on Helpwriting.net ... (see appendix 4) 3. k. 4. l. Proposal for Reasonable Investment: * m. From point 2, we've seen than securing the lease would allow the expected value to increase by 3.1725 million. This is therefore the maximum value we're ready to pay based solely on the main expected value. Nevertheless, if the risk appetite is lower, we could calculate a value that ensures none of the options to have negative return (see tree in appendix 5). This value is equal to 1.3 million. 5. 8. Sensitivity Analysis 1. n. Strategy Change with Probability Change: * o. A one–way sensitivity analysis has been done first based on a whatif table and then, thanks to decision tree tools (appendix 6). This first analysis shows that the breakeven point is at 48%, meaning that if Anders is off by a few percents on his estimate on the result of the lawsuit, not exercising the options becomes the best choice. Regarding the reputation, the breakeven point occurs at 68% (see appendix 7). We have some more margins in that case but the conclusions are the same. A two–way analysis (see appendix 8) shows the safest areas. The (50%– 75%) is close to grey area where a small offset in the probability might change the decision. 2. p. 3. q. Recommendation: * r. Based on here above comments, if it is not possible to assess more clearly the probability (by securing the lease or through a market ... Get more on HelpWriting.net ...
  • 83.
  • 84. Project Management Study Guide Essay Decision Trees – Chelst Chapter 10 Exercises – Kimberly Matthews 10.1 – Sequential decisions: Present an example of a sequence of two or more decisions followed by an uncertainty. Should we open a bakery or a diner? If we open a bakery, should we sell specialty items, like wedding cakes, or sell a variety of baked goods? If we open a diner, should we be open from 6am – 11pm daily or should we be open 24 hours? 10.2 – Information gathering and decisions: Think of a decision scenario where decisions are interspersed with random events. Avon wants to invent their own line of long–wearing lipstick (8+ hours). * Other companies already offer long–wearing lipstick, so will this just saturate the market? * Can Avon come up with ... Show more content on Helpwriting.net ... It is sensitive enough that a 5% decrease in the variable cost of the low investment strategy will cause a shift in the optimal strategy at around $25.90. b) What do you notice with regard to the slope? The slope is zero for the high investment strategy because the variable cost of low investment does not affect it. The slope of the low investment strategy is negative. As the variable cost of the low investment increases, the expected value decreases. 10.5 a, b, d and F – a) Calculate the net profit of each combination of decision and competitor action. Down Home opens a drive–thru with no competition: $84,000 Down Home opens a drive–thru with competition: $44,000 Down Home serves breakfast with no competition: $113,000 Down Home serves breakfast with competition: $33,000 b) What is the best alternative if no competitor opens nearby? Serving Breakfast. What is the best alternative if a competitor opens nearby? Open a drive–thru. d) What decision should the company follow and what is the expected value? He should serve breakfast, because his expected value will be $85,000. f) Recall that the owner treated the layout redesign the same as other annual costs. Would the decision change if he considered only 50% of these redesign costs this year? He should change to doing a drive–thru service because his expected value would increase to $120,000. 10.6 b and c b) Should Red Hen
  • 85. ... Get more on HelpWriting.net ...
  • 86.
  • 87. Freemark Abbey Winery Case Baur Bektemirov BUSF 36106: Assignment 5 Freemark Abbey Winery Assume that under no unusual circumstances (no storm), Jaeger sells 1,000 cases of Riesling. Consider different cases: 1. Jaeger harvests grapes in anticipations of storm. Then the total revenue will be equal to 12×1000×$2.85 = $34200. 2. Jaeger doesn't harvest and there is no storm with 50% chance. 2.1. With 40% chance, sugar concentration is 25%, then the total revenue is 12 × 1000 × $3.50 = $42000 2.2. With 40% chance, sugar concentration is 20%, then the total revenue is 12 × 1000 × $3.00 = $36000 2.3. With 20% chance, sugar concentration is below 20%, then the total revenue is 12 × 1000 × $2.50 = $30000 3. There is storm with 50% chance 3.1. Storm causes botrytis ... Show more content on Helpwriting.net ... MicroPharma will receive 75% of sales in the U.S. and only 10% from sales overseas. v. A firm receives profit for the 10 years after if launches its product in 2003, starting from year 2004. During first years, the sales are increasing in an arithmetic progression starting from 0 in 2003 and reaching the peak in 2008. Since all numbers are in constant dollars, the total sales equal to the sum of sales in each year. If MegaPharma decides not to buy rights or license from MicroPharma, there is 50% chance of successful Phase 2 and 80% chance of successful Phase 3 (conditional on success of Phase 2), and 100% of success in FDA Review. Thus, MegaPharma has 40% chances that the the compound will be approved. If it passes Phase 2, Phase 3, and Review, MegaPharma will spend $52 million. Total MegaPharma's anticipated sales of the compound are equal to 100 + 200 + 300 + 400 + 500 + 500 + 500 + 500 + 500 + 500 = 4000 million, total revenue is 75% × 4000 = 3000 million, if MegaPharma is the only supplier. If both MegaPharma and MicroPharma are on the market, the anticipated revenue is only a half: $1500 million. Total MicroPharma's anticipated sales in the US are equal to $3660 million, and the revenue is $2745 million; anticipated sales overseas are equal to $3660 million and the revenue is $366 million, thus, 3111, if MicroPharma is the monopoly and 3111/2 if it has to share the market with ... Get more on HelpWriting.net ...