DAT 520 Milestone One Guidelines and Rubric For your p.docx

DAT 520 Milestone One Guidelines and Rubric
For your project, choose a data set from the curated list of
sources (Final Project Topics and Sources.xls), or you may
submit your proposal for a different data
source than these listed. Refer to the Final Project Notes
document in the Assignment Guidelines and Rubrics folder for
further guidance on this milestone.
Your submission should include all of the following:
to the decision analysis
techniques you will employ.
question.
its relationship is to the research question.
Guidelines for Submission: Submit 1 to 2 pages of writing
explaining your intended research question. The document
should be double spaced, using 12-point
Times New Roman font and one-inch margins. Your submission
should include your selected data set and explanation for
choosing it and your research question
and explanation for choosing it.

Critical Elements Proficient (100%) Needs Improvement (70%)
Not Evident (0%) Value
Research
Question
Res earch ques ti on has the qual i ti es of bei ng
appropri ate for deci s i on anal ysis techni ques
Res earch ques ti on needs s ome rework to
become appropri ate for deci s i on analys is
No res earch ques ti on i s provi ded, or i s not
vi abl e for deci s i on anal ysis
30
Data Set Choice Res earch ques ti on and chos en data s et(s )
are
compl ementary
Res earch ques ti on and data s et(s ) are
parti al ly compl ementary
Data s et(s ) and res earch ques ti on are not
appropri ate for each other
30
Explanation Characteri zes the chos en data s et(s ), s ource,
and how i t rel ates to the res earch ques ti on.
Li mi ted characteri zation of the data and

s ources
No characteri zation of the data and s ources
was provi ded
30
Articulation of
Response
Submi s s i on has no major errors rel ated to
grammar, s pel l i ng, s yntax, or organi zati on
Submi s s i on has major errors rel ated to
that negati vel y i mpact readabi l ity and
arti cul ation of mai n i deas
Submi s s i on has criti cal errors rel ated to
that prevent unders tandi ng of i deas
10
Earned Total 100%
DAT 520 Milestone Two Guidelines and Rubric

In this milestone, you will create a first draft of your decision
tree. This task assumes you have already established a viab le
research question, identified a data
set, and performed the necessary data prep. To complete this
milestone, you may have to exp eriment/iterate with different
modeling styles. The main objective
of this milestone is to draft your model, explain what you did,
and explain why it is the best model for your research questi on.
For more details on completing
this milestone, refer to the Final Project Notes in the
Assignment Guidelines and Rubrics folder.
Your submission should include the following:
decision tree model that you choose.
analysis process, in an outline or bulleted format. This should
be clear, concise, and
thorough.
discussion of whether or not the results are reasonable and the
model is accurate, whether or
not there are elements that are not present or are needlessly
present, and any errors that may be present.
Guidelines for Submission: Write 2 to 3 double-spaced pages
explaining your model(s). Append the graphical representations
of your model, and any other
supporting material that you feel is necessary to explain this
draft model. Include any sources at the end and cite them in

APA format.
Structure Deci s i on tree and des cri pti on are
cl earl y s tructured
Deci s i on tree and des cri pti on are
s omewhat cl earl y s tructured
Deci s i on tree and des cri pti on are not
adequatel y s tructured
30
Process
Documentation
Documentati on i s cl ear, thorough, and
l ogi cal
Documentati on i s not ful l y cl ear, or
l eaves unexpl ai ned gaps
Documentati on i s not cl ear 30
Evaluation of Results Eval uati on cons i ders reas onablenes s ,
accuracy, mi s s ing/extraneous
el ements , and error i n the model
Eval uati on does not ful l y cons i der
reas onabl enes s , accuracy,

mi s s i ng/extraneous el ements , or error
i n the model
Eval uati on does not cons i der
mi s s i ng/extraneous el ements , or error
i n the model
30
Articulation of
Response
Submi s s i on has no major errors
rel ated to grammar, s pel l i ng, s yntax,
or organi zati on
grammar, s pel l i ng, s yntax, or
organi zati on that negati vel y i mpact
readabi l ity and arti culation of mai n
i deas
Submi s s i on has criti cal errors rel ated
to grammar, s pel l i ng, s yntax, or
organi zati on that prevent
unders tandi ng of i deas
10
Earned Total 100%

DAT 520 Milestone Three Guidelines and Rubric
In this milestone, you will perform an evaluation of your
decision model and revise your decision model as needed.
Evaluation examples are if you are
performing a bottom-up style recursive partitioning analysis,
and you should report on the error rate and variable selection.
You might also consider alternative
variable categorizations to improve your model. If you are
performing a top -down decision tree modeling exercise, what
are the threshold values that cause the
tree to flip? You should perform sensitivi ty analysis on the
critical variables in your tree and report what those sensitivity
analyses are telling you. For either sty le
of modeling, what makes your tree stronger? What breaks the
model? For more information on completing this milestone,
please ref er to the Final Project
Notes in the Assignment Guidelines and Rubrics folder.
Specifically, the following critical elements must be addressed
in your final submission:
the structure of your revised decision tree, with a
clear description.
that is specific to your revised model. In your evaluation,
reflect on the appropriateness
and adjustments of the revised model, as well as the accuracy of

the results you obtained.
Guidelines for Submission: This milestone should be 2 to 3
double-spaced pages of text, with tree model images and any
other supporting material appended.
Review your work to ensure that there are no major errors in
writing mechanics. If you have citations, include the sources at
the end and cite them APA format.
Structure Deci s i on tree and des cri ption are cl early
s tructured
Deci s i on tree and des cri ption are
s omewhat cl early s tructured
Deci s i on tree and des cri pti on are not
adequatel y s tructured
30
Evaluation of Results Eval uati on cons i ders reas onablenes s ,
accuracy, mi s s ing/extraneous el ements ,
and error i n the model
Eval uati on does not ful l y cons i der
mi s s i ng/extraneous el ements , and error

i n the model
Eval uati on does not cons i der
mi s s i ng/extraneous el ements , and error
i n the model
30
Model Diagnostics Model i ncl udes cl ear us e of di agnos ti
cs Model bui l ds i n parti al us e of
di agnos ti cs
Model does not i ncl ude di agnos ti cs 30
Articulation of
Response
Submi s s i on has no major errors rel ated
to grammar, s pel l i ng, s yntax, or
organi zati on
organi zati on that negati vel y i mpact
readabi l ity and arti culation of mai n
i deas
Submi s s i on has criti cal errors rel ated to
organi zati on that prevent

unders tandi ng of i deas
10
Earned Total 100%
Sheet1DomainTopicResearch QuestionData Source #1Data
Source #2FirefightingWildfiresWhat is the human-caused
ignition source that presents the largest potential loss of acreage
for CAL FIRE
annually?http://www.fire.ca.gov/fire_protection/fire_protection
_fire_info_redbooks.phpHealthcareHealth programsWhich of
these programs will be most valuable to implement first?
(Diabetes, congestive heart failure (CHF), asthma, chronic
obstructive pulmonary disease (COPD), and hypertension.)
Corollary to that is the development sequence for which
program will be built next until all five programs are
completed.www.ccwdata.orgEducationAP testsIf a student is
only going to take one AP math or science course while in high
school, which one should he or she
take?http://media.collegeboard.com/digitalServices/pdf/research
/2013/National_Summary_13.xlsReal EstateReal estateIs it
worth the investment to hire a bicoastal real estate company to
assist in expanding the investor's real estate
collection? https://www.census.gov/construction/chars/mfu.html
http://www.zillow.com/research/data/SportsFootballWhat in-
game activity is required to win in the National Football
League? https://www.microbro.com/pfa.htm#EducationEducatio
n fundingDo states whose public schools achieve certain
interquartile performance levels, as defined by combined
fourth- and eighth-grade reading and math scores, exhibit any
common traits in per-pupil expenditures in the categories of
instructor wages, instructor benefits, pupil support,
instructional staff support, general administration, and school
administration expense?

http://www2.census.gov/govs/school/12f33pub.pdfhttp://nces.e
d.gov/nationsreportcard/states/FinanceFailed banksCan we say
which geographic region will have the greatest chance of
experiencing the most bank failures in the
future?https://www2.fdic.gov/SDI/SOB/EducationStudent
performanceHow does the level of faculty effectiveness affect
student performance in reading and mathematics, and can this
be used to help a particular school focus on the faculty members
teaching a particular
subject? https://catalog.data.gov/dataset/chicago-public-
schools-elementary-school-progress-report-card-2012-
2013http://cps.edu/Schools/Pages/Scorecards.aspxElectionsPred
icting electionsCan demographic changes in population be
utilized in lieu of polling for the purpose of predictive electoral
analysis?CNN/ORC
Internationalhttp://www2.census.gov/govs/school/12f33pub.pdf
SportsBaseballAre winning teams built based on pitching or
hitting?http://www.seanlahman.com/baseball-
archive/statistics/Social ServicesThe homelessBased on the
data, which subpopulation (chronically homeless
individuals/families, chronic substance abuse, HIV/AIDS,
severely mentally ill, veterans, and/or victims of domestic
violence) should be targeted to provide more services in
Louisiana?https://www.hudexchange.info/reports/CoC_PopSub_
State_LA_2012.pdfEducationStudent loansWhat type of schools
have undergraduate students that need to borrow the most
amount of subsidized and unsubsidized loans?
https://studentaid.ed.gov/about/data-center/student/title-
ivCensus data re: county populationsReal EstateHome
valuesHow do certain modifications affect home
valuation?http://www.census.gov/programs-
surveys/ahs/data.2011.htmlFoster careAt-risk youthWhich high-
risk foster care youth category is most amenable to an
intervention strategy?
http://www.acf.hhs.gov/sites/default/files/cb/nytd_data_brief_3
_071514.pdffindyouthinfo.govEducation/HealthChildhood

obesityTo which state or pair of states should money for
childhood obesity interventions
go?https://catalog.data.gov/dataset/annual-survey-of-school-
system-financeshttp://www.ncsl.org/research/health/childhood-
obesity-trends-state-rates.aspxHealthcare
financeReimbursementsHow to determine if it would be
possible to function solely on Medicare/Medicaid
reimbursement or if they need to contract with private payers
that are prevalent in the
area.https://data.cms.gov/Medicare/Inpatient-Prospective-
Payment-System-IPPS-Provider/97k6-zzx3HousingConstruction
spendingWhich four construction projects will boost the
economy and whether the relative standard error for total
construction is less than 3% because anything higher would
cause a loss. The four construction categories are residential,
non-residential commercial, educational, and
healthcare.data.govEducationParental involvementAre parents'
education level and their involvement in their child’s education
good predictors for a student’s success in school?
http://nces.ed.gov/nhes/dataproducts.asp#2007dphttp://michigan
.gov/documents/Final_Parent_Involvement_Fact_Sheet_14732_
7.pdfhttp://www.fire.ca.gov/fire_protection/fire_protection_fire
_info_redbooks.phphttp://www.ccwdata.org/http://media.college
board.com/digitalServices/pdf/research/2013/National_Summary
_13.xlshttps://catalog.data.gov/dataset/chicago-public-schools-
elementary-school-progress-report-card-2012-
2013http://cps.edu/Schools/Pages/Scorecards.aspxhttps://student
aid.ed.gov/about/data-center/student/title-
ivhttp://www.census.gov/programs-
surveys/ahs/data.2011.htmlhttp://michigan.gov/documents/Final
_Parent_Involvement_Fact_Sheet_14732_7.pdfhttps://www.cens
us.gov/construction/chars/mfu.html
DAT 520 Final Project Notes
This document is intended to assist you with the final project.

This course introduces a decision analysis using a decision tree.
There are guidelines that can assist in completing a project of
this kind.
Milestone One: Choose a Data Set and Formulate Decision
Analysis Research Question
In Milestone One, you are to provide an abstract describing
your decision question and high level approach. Consider this
abstract as the information that your peers will read and
determine if they want to read more. It needs to include a
statement of your decision question, what your premise is, and
how the data will be used to support the analysis. The
information that follows are additional notes for Milestone One:
In terms of data, the data will lend itself to a decision-making
scenario with discrete outcomes. As additional information on
how to select your data, first think about the course materials
and what you have learned so far about the types of situations
that lend themselves to decisions under uncertainty. Then, use
the table of student projects as a seed to see what other people
have worked on in the past.
Selecting your data set is step one, but it also overlaps step two,
which is formulating a research question. Decision analysis
research questions are best stated as a discrete set of choices to
be analyzed. A good decision analysis research question also
possesses the following hallmarks:
· It has clarity of purpose by being framed as a discrete set of
choices to be analyzed.
· It is concise.
· It is appropriate and can be answered with decision analysis
techniques.
· Its parts are relevant to each other.
· It has not been answered before, or the variation from existing
works is great enough to make it a novel line of inquiry.

· It is open-ended enough that it may lead to new research
questions.
· But it is closed enough that direct answers are possible, even
if they may not be found by this round of analysis.
Here are two references that will guide you in writing a good
decision analysis research question: Writing Research
Questions, What Makes a Good Research Question? The
discussion forum is also available to ask the instructor and
classmates about your research question. The guiding light,
however, should be what interests you most and makes you want
to investigate.
Milestone Two: Develop Decision Analysis Model
During the last milestone, you may have been thinking about
different ways to create a model that explains your thoughts. By
this point in the class, you have been exposed to both top-down
and bottom-up modeling styles. To complete this milestone, you
may have to experiment with different modeling styles. The
main objective is to draft your model, explain what you did, and
explain why it is the best model for your research question. Are
you leaving out any variables that could strengthen your model?
Figure out the style of decision analysis modeling that you
might use toward exploring your research question. At your
discretion, your analysis may include some observational data
analysis methods that you learned in experiences outside of this
class, but the bulk of your methods need to have been taught in
this class. Be careful not to let other methods overpower what
you are doing here with decision analysis. More than 75% of
your project needs to be decision analysis modeling as covered
in DAT 520. For example, it will not be acceptable to revert to a
regression analysis to complete your final project.
That said, how do you go about creating a model? First, you
need to have a viable decision analysis research question. In

other words, you need a research question that analyzes a
discrete set of choices. Second, you need to have at least one
viable data set: it needs to contain the variables and covariates
of interest. Or, if you have multiple data sets, they need to be
combined. You have been learning skills in R that will assist
you in this data prep phase. If R is still uncomfortable, you can
always use Microsoft Excel. The data set that you end up with
needs to be cleaned of errors, and ready to go for use in Rattle,
or in R to create probabilities. Third, and this is the most
critical, you need to decide whether you are going to engage in
a bottom-up or top-down modeling style.
To decide if you are going to use a top-down model, you need to
be able to create proportions from your data set that represent
the decision nodes and chance nodes that fall on the path
between outcomes and choices. Or, if you are going to use a
bottom-up model, you need to decide how many groups should
be represented in the outcome of interest. Is that outcome
represented as a continuous value in the data set? If it is, then it
will need to be converted into a categorical variable. Consult
your references for how to do this efficiently. Also keep in
mind Rattle’s setting for the number of buckets. Getting the
variables ready, either as proportions for top-down modeling or
as categorical variables for bottom-up modeling, is the most
basic aspect of the data prep that you need to do.
To draw the model, follow the guidance for either Rattle or
TreePlan. You should have done enough examples as well as the
assignment to know where to start. A good decision analysis
model will have more than just choice and outcome, or choice
one chance node and outcome. It will be multifaceted. It will
consider a number of inflection points that occur in making that
decision, somewhere between three and 20. Most decision trees
have somewhere between three and seven levels, so you should
aim for approximately that level of complexity.

This milestone is important because it should be making you
curious about your research project. What is the best way to
investigate this research question? Are there alternative ways to
draw the same model? What happens if you include this
variable? What happens if you exclude this one but include this
other one? What happens if you tried both the top-down and
bottom-up model? Does your research question support either
approach? Does your model match up with what your research
question is asking?
The next part to consider is whether the graphical representation
of the model is clear or not. Are the parts labeled clearly? Are
the values present? Is there anything missing? Is the optimal
path obvious? Do you know what the errors are? Is the model
interesting? There are a number of different considerations, but
the main outcome of this milestone is to experiment to discover
the best way to answer your research question.
Milestone Three: Revise and Evaluate Decision Analysis Model
In this milestone, you will perform and evaluation of your
decision model and revise your decision model as needed.
Evaluation examples are if you are performing a bottom-up
style recursive partitioning analysis, you should report on the
error rate and variable selection. You might also consider
alternative variable categorizations to improve your model. If
you are performing a top-down decision tree modeling exercise,
what are the threshold values that cause the tree to flip? You
should perform sensitivity analysis on the critical variables in
your tree and report what those sensitivity analyses are telling
you. For either style of modeling, what makes your tree
stronger? What breaks the model?
· If you are performing a bottom-up style recursive partitioning
analysis, you should report on the error rate and variable
selection, and what you did to improve them. You might also
consider alternative variable categorizations to improve your

model. You might consider creating different versions of the
same variable with slightly different categories and invoking
them selectively in Rattle. You might consider making multiple
models that represent different groups of variables to explain an
answer to the research question slightly differently each way.
You should also report shifts in the error rate and what that
means when you do different things.
· If you are performing a top-down decision tree model, where
are the threshold values that cause the tree to flip? Are there
any? You have learned about sensitivity analysis at this point in
class, so you should be able to identify the critical values for
key variables in your tree and report what the sensitivity
analyses are telling you. What happens when you include
certain decision nodes in your tree but exclude others? Can you
draw alternative trees that still answer the research question?
What happens to the proportions and the outcomes? What
method are you going to use to deduce the optimal path?
Generally, for any of these decision trees, what makes your tree
stronger? What breaks the model? What kinds of variables do
you wish you had but do not have data for? What is the best
criticism of the tree that you drew? What are its limitations?
What are its strengths? You do not need to answer all of these
questions exhaustively, but can use them as launching points for
your writing.
Final Submission: Decision Analysis Model and Report
For your final project, you will submit your decision analysis
model and report, compiling all the components used to develop
the model and produce the report, as well as a leading abstract,
table of contents, and in a format that addresses all of the
critical elements in the instructions. Each of the prior
milestones has information needed to produce the final report.
Note, the final report is not just a concatenation of the
milestones. It instead is a report that is created within a defined

eight page limit that will include sections that detail the
limitations and justification for your analysis. You should also
take the time to address any ethical or legal issues that connect
with your results or decisions being analyzed. Lastly, you
should address the agility of your analysis and how it might be
applied to future uses.
DAT 520 Final Project Guidelines and Rubric
Overview
You must complete a decision analysis research project as your
final project for this course. Your research project will focus on
a real-world topic of your choice,
as approved by your instructor. You will pick a topic from the
list provided or with approval from your instructor, and creat e
a data analysis plan and decision
tree model based on a real-world scenario. This assessment will
provide you with the opportunity to employ highly valued
decision support skills and concepts
for data within a real-world context. You can use the Final
Project Notes document, found in the Assignment Guidelines
and Rubrics section of the course.
The project is divided into three milestones, which will be
submitted at various points throughout the course to scaffold
learning and ensure quality final
submissions. These milestones will be submitted in Modules
Two, Five, and Seven. The final submission will occur in
Module Nine.
This project will address the following course outcomes:

-standard
methods and techniques for its utility in supporting decision
making
ata manipulation and mode ling methods
for decision support
support by applying data manipulation, modeling, and
management concep ts
f
decision-oriented data based on industry standards and one’s
personal ethical criteria
of data-mining procedures for decision support in various
industries
Prompt
Your decision analysis model and report should answer the
following prompt: How does your model and evaluation resolve
uncertainty in making a decision? In
order to produce your analytic report, you will need to choose
and investigate a data set using the decision analysis techniques
you learned in class. Then you
will formulate a research question, write an analytic plan, and
implement it. Your report should not solely consist of
descriptions of what you did. It should also
contain detailed explorations into the meaning behind your
model and the implications of its results. You will also be
testing your model’s fitness and evalu ating
its strengths and weaknesses.
The project in a nutshell:

1. Choose a data set (get ideas from the source list in the
spreadsheet Final Project Topics and Sources.xls)
2. Formulate your decision analysis research question
3. Write an analytic plan
4. Perform the top-down or bottom-up modeling
5. Perform model diagnostics
6. Evaluate
These activities are broken up into milestones so that the work
is spread throughout the term and you can get early assistance
with any obstacles.
A decision analysis report is similar to any other analytic
report. These reports introduce a problem, state a line of inquir
y, explain a model that the author
developed, discuss results and limitations, and then make
conclusions and recommendations. Some decision models seek
the best expected value among a
discrete set of choices. Other decision analyses might seek the
threshold values at which the model changes from o ne
recommendation to another, describe the
implications, and leave it to the reader to decide what to do.
Still other decision models might look for the likeliest path to
explain pat terns that are already
present in a data set. In all cases, they have some thing in
common: They are trying to help resolve uncertainty. Your job
is to bring clarity to the decision being
made.

Decision analysis seeks less to produce a definitive result, and
more to accurately explain the combinations of possibilities that
can lead decision makers to
clearer choices. This is the modeling aspect. If you model the
weather but never take into account barometric pressure, your
model would fail if trying to
determine the worst hurricane trajectories. These are the kinds
of things you will be looking at in your decision models:
searching for ways to explain the
conditions that produce outcomes and to evaluate the strengths
and weaknesses of the models you produce.
The three main ideas that your report should encompass are
your ability to formulate a decision analysis research question
based on an appropriate data set,
develop your model, and finally evaluate the model’s utilities,
results, strengths , and weaknesses. In short, if your report fully
encompasses these three
concepts, you will produce an authentic document that would
stand on its own in a professional setting.
Data sources to choose from: The included spreadsheet lists
data sets used in previous sessions of DAT 520. Students found
these data sets, prepared them for
modeling on their own, and wrote excellent papers on the
topics. Remember that your data set needs to be appropriate for
modeling a discrete set of choices.
Either those choices are built into the model as categorical
variables, or you will need to do some legwork by converting
continuous variables into rational
categorical groups. This activity would be part of the data
preparation and documented in your data appraisal section.
Your final project must include the following sections:

o four pages
Up to one page for graphic(s)
Up to two pages for model explanation
Sources: Note that the core elements add up to about 15–20
pages, double-spaced. The overall target for the core elements is
still 15–20 pages, so that you
have room to adjust each section according to the needs of the
project. Everything you need to say in the report should fit w
ithin 15–20 double-spaced, 12-point
font pages with one-inch margins.
To see some good final projects, consult the exemplars. Not all
of them are 100% perfect papers, but they do embody the level
of complex thinking that

characterizes an interesting project. The idea behind the page
limit is to explore the con cept of “less is more.” If you add up
the text, graphics, sources, and
supporting material from all the milestones, you end up with 15
to 20 pages. For the final, that means some compression needs
to occur. This means finding the
most important information from what you have previously
written and leaving room for the new parts that you need to
write. Follow the list of required
elements for the final to guide how to structure your research
paper.
Specifically, the following critical elements must be met in your
final submission:
I. Introduction: Analyze the purpose, type, intended
populations, and uses of the analysis to establish an appropriate
context for the data-mining and
analysis plan.
II. Data Appraisal
A. Characterize the data set. For example, what is the purpose
such data are generally used for?
B. Appraise the data within the context of the problem to be
solved and industry standards. How will you use the data? For
example, expound
upon the limitations of the data set in the context of your needs.
C. Explain the utilities that you will be using and how the data
supports that choice.
III. Select Appropriate Techniques
A. Determine and explain the appropriate steps for preparation
of the data sets into a usable form: what steps were taken to
make data

descriptions clear, how extreme or missing values were
addressed, and how data quality was improved.
B. Determine the appropriate steps (including: risk assessment,
probability calculations, and modeling techniques) for data
manipulation and in-
depth analysis to support organizational decision-making.
C. Models and checkpoints: How will you optimize the models,
what will you test for, and how will you build in checks to d
etermine a successful
analysis?
D. Defend the ethicality and legality of the analytic selections
made for use, interpretation, and manipulation of the data based
on in dustry
standards for legal compliance, policies, and social
responsibility. If there are no potential ethical and legal
compliance issues, explain how your
prep and use of this data are both ethical and legal.
IV. Defend and Evaluate Choices
A. Why are these choices the best for the data and problem at
hand? What research or industry standards are supportive of
your choices of
methods? Explain how the methods chosen will support
organizational decision-making.
B. Determine the agility of these choices for decision support
based on research and relevant examples: how can they be
adapted to alternative

needs or reapplied to future analysis?
C. Address ethical and legal issues that might arise from the use
and interpretation of the data, based on industry standards,
policies, and social
responsibility. How can you ensure that your selected
procedures, use of data, and results will be socially r esponsible
and in line with your own
ethical standards?
D. Implement your plan: Perform data preparation, mining and
modeling procedures, and create your decision support solution.
V. Decision Tree Model (bottom-up, top-down): Include the
detailed process and programming steps necessary to complete
the analysis. Be sure to:
A. Defend the overall structure and purpose of the tree model in
organizational decision support.
B. Develop process-documentation that addresses potential
complications. This piece should resemble a recipe/outline that
provides enough
information for addressing potential implementation issues.
C. Evaluate the results of your decision tree model. At
minimum, attend to the following:
1. Are the results reasonable?
2. How accurate is your model?
3. Are there missing or extraneous elements that could have
influenced your results?
4. What common errors are made during creation of the model
you chose? How did you ensure that you did not make these
errors?
VI. Articulation of Response/Final Report: Utilizes
visualization options that effectively address the needs of the

audience. Options may include annotated
shell tables, visualizations, and a compositional structure.
To guide you in writing your final paper, follow the Final
Project Rubric. The rubric is less about format and more about
thought. Specifically, you should write
sections that detail the limitations and justification for your
analysis. You should also take the time to address any ethica l
or legal issues that connect with your
results or decisions being analyzed. You should annotate and
caption your graphics. You could include a table that characteri
zes the data set. You should address
what your model does to assist decision makers. You should
defend your choices of variables and groupings. Lastly, you
should address the agility of your
analysis and how it might be applied to future uses.
Milestones
Milestone One: Choose a Data Set and Formulate Decision
Analysis Research Question
In Module Two, you will choose a data set from the curated list
of sources (Final Project Topics and Sources.xls), or you may
submit your proposal for a different
data source than those listed. Then you will write a decision
analysis research question, which should be two to three pages
in length and framed as a discrete
set of choices to be analyzed. This milestone is graded with the
Milestone One Rubric.
Milestone Two: Develop Decision Analysis Model
In Module Five, you will draft your decision tree. This task
presupposes a data set, a viable decision analysis research
question, and the necessary data prep. To
complete this milestone, you may have to experiment with

different modeling styles. The main objective is t o draft your
model, explain what you did, and
explain why it is the best model for your research question. This
milestone is graded with the Milestone Two Rubric.
Milestone Three: Revise and Evaluate Decision Analysis Model
In Module Seven, you will revise and evaluate your decision
model based on the feedback you received from the instructor
for the previous milestone.
Evaluation in this case could mean a few different things. If you
are performing a bottom -up style recursive partitioning
analysis, you should report on the error
rate and variable selection. You might also consider alternative
variable categorizations to improve your model. If you are p
erforming a top-down decision tree
modeling exercise, what are the threshold values that cause the
tree to flip? You should perform sensitivity analysis on the
critical variables in your tree and
report what those sensitivity analyses are telling you. For either
style of modeling, what makes your tree stronger? What bre aks
the model? This milestone is
graded with the Milestone Three Rubric.
Final Submission: Decision Analysis Model and Report
In Module Nine, you will submit your decision analysis model
and report, compiling all the components used to develop the
model and produce the report, as
well as a leading abstract, table of contents, and in a format that
addresses all of the critical elements in the instructions. The
project should include sections

that detail the limitations and justification for your analysis.
You will probably be compressing what you wrote for your
introduction to make it fit within the
eight-page limit. You should also take the time to address any
ethical or legal issues that connect with your results or
decisions being analyzed. Lastly, you should
address the agility of your analysis and how it might be applied
to future uses. This assignment is graded with the Final Project
Rubric.
Deliverables
Milestone Deliverables Module Due Grading
One Research Question Two Graded separately; Milestone One
Rubric
Two Develop Decision Analysis Model Five Graded separately;
Milestone Two Rubric
Three Revise and Evaluate Model Seven Graded separately;
Milestone Three Rubric
Decision Analysis Model and Report Nine Graded separately;
Final Project Rubric
Final Project Rubric
Guidelines for Submission: The final report will be a 15–20
page research paper, double-spaced, in 12-point Times New

Roman font with one-inch margins all
around and APA citations. Title page, abstract, appendices and
bibliography of sources are extra beyond the 15–20 pages of the
report. You may include one
page or less of annotated/captioned graphics as part of the
report. The purpose of the limits is to keep the discussions
compact and to maintain the integrity o f
publication-quality research.
Critical Elements Exemplary (100%) Proficient (90%) Needs
Improvement (70%) Not Evident (0%) Value
Introduction Meets “Profi ci ent” cri teri a and
ci tes s peci fi c, rel evant exampl es
to es tabl i s h a robus t context for
the data-mi ni ng anal ys is pl an
The purpos e, type, i ntended
popul ati ons , and us es of the
anal ys is report are anal yzed to
es tabl i s h an appropriate
context for the data -mi ni ng
anal ys is pl an
The purpos e, type, i ntended
popul ati ons , and us es of the
anal ys is report are not
s uffi ci entl y analyzed to
es tabl i s h an appropriate
context for the data -mi ni ng
anal ys is pl an
Ei ther the purpos e, type,

i ntended popul ati ons , or us es
of the anal ys is report are not
anal yzed
6.25
Data Appraisal:
Characterize
Meets “Profi ci ent” cri teri a and
cl ai ms are qual ified wi th s ource
evi dence or exampl es
Makes accurate cl ai ms about
the general us e of the
datas et(s ) and the i ntended
purpos e of the data
Not al l cl aims about the general
us e of the datas et(s ) and the
i ntended purpos e of the data i s
accurate gi ven the avai l able
evi dence
Does not make cl ai ms about the
general us e of the datas et(s )
and the i ntended purpos e of
the data
6.25
Data Appraisal:

Context
qual i fi es claims s pecific to
di s crete needs of the
organi zati on
Makes accurate cl ai ms about
the data wi thi n i ndus try
s tandards and the context of
the probl em to be s ol ved
Not al l cl aims about the data
are accurate bas ed on i ndus try
s tandards and the context of
the probl em to be s ol ved
data bas ed on the context of
the probl em to be s ol ved and
i ndus try s tandards
6.25
Data Appraisal:
Measurable Utilities
s upporti ng expl anati on i s
qual i fi ed wi th exampl es or
res earch evi dence

Makes accurate determi nati on
and thoroughl y expl ai ns the
meas urabl e uti l i ti es and how
the data s upports that choi ce
Determi nati on of uni t of
anal ys is i s not enti rel y accurate
or expl anati on does not
thoroughl y expl ai n how the
data s upports meas urabl e
uti l i ti es determi nati on
Does not determi ne a
meas urabl e uti l i ti es
6.25
Select Appropriate
Techniques:
Preparation
qual i ty of expl anati on al l ows for
a s eaml es s del i very of the i ni ti al
mol di ng proces s
Makes appropri ate anal ysis s tep
s el ecti ons and expl ai ns the
proces s for prepari ng the raw
data

Not al l anal ysis s tep s el ecti ons
are appropri ate for prepari ng
the raw data, or not al l s tep
proces s es are s uffi ci entl y
expl ai ned
Does not s el ect and expl ai n
anal ys is s teps for prepari ng raw
data i nto a us eabl e form
6.25
Select Appropriate
Techniques:
Manipulation
s tep s el ecti on and expl anati ons
are s eaml es s l y i ntegrated i nto a
cl ear proces s
Makes appropri ate s tep
s el ecti ons and expl ai ns the
proces s of s teps for i n-depth
anal ys is and mani pul ati on of
the data to s upport
organi zati onal deci s ion maki ng

Not al l s teps are appropri ate for
i n-depth anal ys is and
mani pul ati on i n s upport of
organi zati onal deci s ion maki ng
or not al l s teps are expl ai ned i n
terms of proces s
Does not s el ect and expl ai n i n-
depth anal ys is and
mani pul ati on s teps for deci s i on
s upport
6.25
Select Appropriate
Techniques:
Checkpoints
the expl anati ons of the
s el ecti ons provi de cl ear and
s eaml es s i ntegrati on of s teps
i nto the overal l mani pul ati on
proces s
Makes appropri ate al gori thm
s el ecti ons , and expl ai ns the
proces s of the s el ecti ons , for
the opti mi zati on, ri s k

as s es s ment, and bui l t-i n check
poi nts to ens ure the s ucces s of
data anal ys is and mani pulation
Not al l al gorithm s el ecti ons and
expl anati ons of proces s for
opti mi zati on, ri s k as s es sment,
and bui l t-i n check poi nts are
appropri ate to ens ure
s ucces s ful data analysis and
mani pul ati on, or key val uabl e
methods are mi s s ed
Does not s el ect and expl ai n the
proces s of al gori thm s el ecti ons
for opti mi zati on, ri s k
as s es s ment, and bui l t-i n
checkpoi nts
6.25
Select Appropriate
Techniques: Defend
s ubs tanti ates cl aims wi th
s chol arly res earch evi denci ng
cons i derati ons of s oci al
res pons i bi lity
Makes and jus ti fi es cl aims

about the ethi cal and l egal
i s s ues rel ated to the us e,
i nterpretati on, and
mani pul ati on of the data for the
deci s i ons bei ng made, bas ed on
i ndus try s tandards , l aws, and
organi zati onal pol icies
Not al l cl aims about the ethi cal
and l egal i s s ues rel ated to the
us e, i nterpretati on, and
deci s i ons bei ng made are
jus ti fi abl e bas ed on i ndus try
s tandards , l aws , and
organi zati onal pol icies
ethi cal and l egal i s s ues rel ated
to the us e, i nterpretati on, and
deci s i ons bei ng made
6.25
Defend and Evaluate
Choices: Best

res earch i n s peci fi c s upport of
the deci s i ons /problem at hand
about the appropri atenes s of
the methods for mani pul ati on
and al gori thm s el ecti ons made
for deci s i on s upport bas ed on
anal ys is of i ndus try s tandards
and val i d res earch
Not al l cl aims about the
appropri atenes s of the methods
for mani pul ati on and al gorithm
s el ecti ons made are jus ti fi able
bas ed on anal ys i s of i ndus try
s tandards and val id res ea rch
Does not make and jus ti fy
cl ai ms about the
appropri atenes s of the methods
for mani pul ati on and al gorithm
s el ecti ons made
6.25
Defend and Evaluate
Choices: Agility
s chol arly res earch and real

worl d exampl es
about the agi l i ty of the choi ces
made for deci s i on s upport i n
vari ous i ndus tries , projects , and
organi zati ons wi th res earch and
rel evant exampl es
Not al l cl aims about the agi l i ty
of the choi ces made for
deci s i on s upport i n vari ous
i ndus tri es , projects , and
organi zati ons are jus ti fiable
bas ed on the provi ded res earch
and exampl es
agi l i ty of the choi ces made for
deci s i on s upport i n vari ous
i ndus tri es , projects , and
organi zati ons
6.25
Defend and Evaluate
Choices: Address
Issues

the detai l s of the expl anati on
expound upon s oci al
res pons i bi lity and i ndus try
s tandards
Detai l s the ethi cal
cons i derati ons that s houl d be
made about us e of the res ul ts
of the s ol uti on and how ethi cal
us e can be ens ured
Expl ai ns ethi cal cons iderati ons
for us e of the res ul ts of the
s ol uti on, but l acks detai l or
does not expl ai n how ethi cal
us e can be ens ured
Does not expl ai n ethi cal
cons i derati ons for us e of
s ol uti on res ul ts
6.25
Decision Tree
Model: Implement
performance of mi ni ng proces s
and accuracy of deci s i on

s ol uti on evi dence appropri ate
pl anni ng and i mpl ementati on of
pl an wi thi n the context of the
s el ected topi c
Correctl y performs the data
mi ni ng proces s and creates an
accurate deci s i on s upport
s ol uti on
Performs the data mi ni ng
proces s and creates a deci s i on
s upport s ol uti on, but s ol uti on i s
not accurate
Does not perform the data
mi ni ng proces s and create a
deci s i on s upport s ol uti on
6.25
Decision Tree
Model: Structure
s chol arly evi dence and real
worl d exampl es
about the overal l s tructure and
purpos e of model for

organi zati onal deci s ion s upport
bas ed on s peci fi c exampl es and
res earch
Not al l cl aims about the overal l
s tructure and purpos e of model
for deci s i ons s upport are
jus ti fi abl e
overal l s tructure and purpos e of
model for organi zati onal
deci s i on s upport
6.25
Decision Tree
Model:
Documentation
model i s of qual i ty to al l ow
others to devel op further, more
detai l ed model s to addres s
pos s i bl e i s sues
Outl i ne effecti vel y acts as
proces s documentati on for
addres s i ng potenti al
compl i cati ons duri ng
i mpl ementati on of the anal ys i s
pl an

Not al l as pects of outl i ne woul d
be effecti ve i n addres s i ng the
potenti al compl i cati ons of
i mpl ementati on, or common
major i s s ues are not addres s ed
Does not i ncl ude an outl i ne for
addres s i ng potenti al
compl i cati ons duri ng
i mpl ementati on
6.25
Decision Tree
Model: Results
comprehens i vel y eval uates
agai ns t cri teri a above the gi ven
cri teri a and s peci fi cally rel evant
to the context of the s el ected
topi c
Accuratel y eval uates the res ul ts
of the deci s i on tree model
agai ns t the gi ven cri teri a
Eval uates the res ul ts agai nst the

gi ven cri teri a, but wi th gaps i n
accuracy
Does not eval uate the res ul ts
agai ns t the gi ven cri teri a
6.25
Articulation of
Response
Submi s s i on i s free of errors
rel ated to ci tati ons , grammar,
s pel l i ng, s yntax, and
organi zati on and i s pres ented i n
a profes s i onal and eas y to read
format
Submi s s i on uti l izes vi s ualization
opti ons that effecti vel y addres s
the needs of the audi ence and
has no major errors rel ated to
ci tati ons , gra mmar, s pel l i ng,
s yntax, or organi zati on
Submi s s i on uti l izes various
vi s ual izati on opti ons that don’t
effecti vel y addres s the needs of
the audi ence or has major
errors rel ated to ci tati ons ,
organi zati on that negati vel y

i mpact readabi l i ty and
arti cul ation of mai n i deas
Submi s s i on does not uti l i ze
vi s ual izati on opti ons for the
audi ence or has cri ti cal errors
rel ated to ci tati ons , grammar,
s pel l i ng, s yntax, or organi zati on
that prevent unders tandi ng of
i deas
6.25
Earned Total 100%

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