Description
Part 2: The final project for this course is the creation of a
statistical analysis report. Each day, management professionals are
faced with multiple decisions affecting various aspects of the
operation.
The ability to use data to drive decisions is an essential skill that is
useful in any facet of an operation. The dynamic environment offers
daily challenges that require the talents of the
operations manager; working in this field is exciting and rewarding.
In Module Seven, you will submit your selection of statistical tools and
data analysis, which are critical elements III and IV of your final
project. You will submit a 3- to 4-page paper that
provides justification for the appropriate statistical tools needed to
analyze the company’s data.
Specifically, the following critical elements must be addressed:Part
3:The final project for this course is the creation of a statistical
analysis report.
Each day, management professionals are faced with multiple decisions
affecting various aspects of the operation. The ability to use data to
drive decisions is an essential skill that is useful in
any facet of an operation. The dynamic environment offers daily
challenges that require the talents of the operations manager; working
in this field is exciting and rewarding.
Throughout the course, you will be engaged in activities that charge you
with making decisions regarding inventory management, production
capacity, product profitability, equipment
effectiveness, and supply chain management. These are just a few of the
challenges encountered in the field of management.
The final activity in this course will provide you with the opportunity
to demonstrate your ability to apply statistical tools and methods to
solve a problem in a given scenario that is often
encountered by a manager. Once you have outlined your analysis strategy
and analyzed your data, you will then report your data, strategy, and
overall decision that addresses the given
problem.
Unformatted Attachment Preview
QSO 510 Final Project Guidelines and Rubric
Overview
The final project for this course is the creation of a statistical analysis report.
Each day, management professionals are faced with multiple decisions affecting various aspects of the operation. The ability to use data to drive decisions is an essential skill that is useful in
any facet of an operation. The dynamic environment offers daily challenges that require the talents of the operations manager; working in this field is exciting and rewarding.
Throughout the course, you will be engaged in activities that charge you with making decisions regarding inventory management, production capacity, product profitability, equipment
effectiveness, and supply chain management. These are just a few of the challenges encountered in the field of management.
The final activity in this course will provide you with the opportunity to demonstrate your ability to apply statistical tools and methods to solve a problem in a given scenario that is often
encountered by a manager. Once you have outlined your analysis strategy and analyzed your data, you will then report your data, strategy, and overall decision that addresses the given
problem.
The project is divided into two 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 Three and Seven. The final project is due in Module Nine.
In this assignment, you will demonstrate your mastery of the following course outcomes:
Apply data-based strategies in guiding a focused approach for improving operational processes
Determine the appropriate statistical methods for informing valid data-driven decision making in professional settings
Utilize a structured approach for data-driven decision making for fostering continuous improvement activities
Propose operational improvement recommendations to internal and external stakeholders based on relevant data
Prompt
Management professionals are often relied upon to make decisions regarding operational processes. Those who utilize a data-driven, structured approach have a clear advantage over those
offering decisions based solely on intuition. Your task is to review the scenario found in your textbook in Chapter 19 at the end of the “From Learning to Earning” section entitled “Brief Case:
Building Models,” the supplemental case study document and the accompanying data set outline the appropriate analysis strategy; select a suitable statistical method; and use data analysis to
ultimately drive the decision. Once this has been completed, you will be challenged to present your data, data analysis strategy, and overall decision in a concise report, justifying your analysis.
Specifically, the following critical elements must be addressed:
I. Introduction to the problem:
Provide a concise description of the scenario that you will be analyzing. The following questions might help you describe the scenario: What is the type of organization identified in the
scenario? What is the organization’s history and problem identified in the scenario?
II. Create an analysis plan to guide your analysis and decision making: In this section you will review the data set to inform the statistical method that will be used. To inform your responses
in this section, calculate the central tendency of the dependent variable, develop a histogram of the gift amount and develop scatterplots for the gift amount for each independent
variable.
A. Identify any quantifiable factors that may be affecting the performance of operational processes.
B. Develop a single sentence problem statement that addresses the given problem in the scenario and contains quantifiable measures.
C. Propose a strategy that addresses the problem of the organization in the given case study.
III. Identify statistical methods to collect data:
A. Identify the appropriate statistical methods that you will use to perform your analysis. What are your statistical assumptions concerning the data that led you to selecting the
methods? In other words, why did you select this method for statistical analysis?
B. Justify why you chose these methods to analyze the data. Be sure to include how these methods will help predict the use of the data in driving decisions using cited evidence.
IV. Data-Driven Decisions to determine the appropriate decision for the identified problem:
A. Outline the process needed to utilize your statistical analysis to reach a decision regarding the given problem.
B. Explain how data mining is used to develop a solution to the case. In your response, consider the concepts of metadata.
C. Explain if the problem is a structured or unstructured problem.
D. Assess how the variables have potential for answering the problem.
V. Recommend operational improvements to stakeholders after you have completed the calculations:
A. Demonstrate a data-driven calculation that addresses the given problem statement.
B. Summarize results of the analysis plan. In your response, be sure to explain the validity of the model solution.
C. Explain how the model solution addresses the given problem statement.
Milestones
Milestone One: Introduction and Analysis Plan
In Module Three, you will submit your introduction and analysis plan, which are critical elements I and II. You will submit a 3-to 4-page paper that describes the scenario provided in the case
study, identifies quantifiable factors that may affect operational performance, develops a problem statement, and proposes a strategy for resolving a company’s problem. This milestone will be
graded with the Module One Rubric.
Milestone Two: Statistical Tools and Data Driven Decisions
In Module Seven, you will submit your selection of statistical methods and data driven decisions, which are critical elements III and IV. You will submit a 3-to 4-page paper and a spreadsheet
that provides justification of the appropriate statistical methods that are needed to analyze the company’s data. This milestone will be graded with the Module Two Rubric.
Final Project Submission: Statistical Analysis Report
In Module Nine, you will submit your statistical analysis report and recommendations. It should be a complete, polished artifact containing all of the critical elements of the final product. It
should reflect the incorporation of feedback gained throughout the course. This submission will be graded with the Final Project Rubric.
What to Submit
Your statistical analysis report must be 10–12 pages in length (plus a cover page and references) and must be written in APA format. Use double spacing, 12-point Times New Roman font, and
one-inch margins. Include at least six references cited in APA format.
Final Project Rubric
Criteria
Exemplary
Proficient
Introduction: Description of
the Scenario
Meets “Proficient” criteria and
description demonstrates
insightful understanding of the
situation described in the
scenario (100%)
Concisely and accurately
describes the scenario (90%)
Analysis Plan: Factors
Meets “Proficient” criteria and
demonstrates insight into
operational processes and
factors that may affect
performance (100%)
Analysis Plan: Problem
Statement
Analysis Plan: Strategy
Needs Improvement
Not Evident
Value
Describes the scenario but
description is not concise or
contains inaccuracies (70%)
Does not describe the scenario
(0%)
4.04
Identifies quantifiable factors
that may be affecting the
performance of operational
processes and supports claims
with explanations (90%)
Identifies quantifiable factors
that may be affecting the
performance of operational
processes but identification is
not supported with
explanations or is cursory
(70%)
Does not identify quantifiable
factors that may be affecting
the performance of operational
processes (0%)
7.66
Meets “Proficient” criteria and
statement demonstrates
Develops a single sentence
problem statement appropriate
Develops a problem statement
appropriate to the scenario
Does not develop a single
sentence problem statement
7.66
insight into the relationship
between the quantifiable
measures and problem
addressed in the scenario
(100%)
to the scenario that addresses
the given problem and contains
quantifiable measures (90%)
that addresses the given
problem but statement does
not contain quantifiable
measures or is inappropriate
(70%)
appropriate to the scenario
that addresses the given
problem (0%)
Meets “Proficient” criteria and
strategy demonstrates nuanced
understanding of the problems
(100%)
Proposes a strategy that
addresses the problem of the
company (90%)
Proposes a strategy but
strategy either does not
address the problem or
strategy is not feasible (70%)
Does not propose a strategy
that addresses the problem of
the company (0%)
7.66
Criteria
Exemplary
Statistical Methods:
Statistical Methods
Meets “Proficient” criteria and
identification demonstrates
nuanced understanding of
statistical tools (100%)
Statistical Methods: Justify
Methods
Meets “Proficient” criteria and
justification demonstrates
insight into the relationship
between statistical methods
and the type of data (100%)
Proficient
Needs Improvement
Not Evident
Value
Identifies the appropriate
statistical methods used to
perform statistical analysis,
including statistical
assumptions (90%)
Identifies a statistical methods
used to perform statistical
analysis but either the methods
are not the most appropriate to
use or discussion lacks
statistical assumptions (70%)
Does not determine statistical
methods (0%)
7.66
Justifies why the methods
chosen are the most
appropriate for analysis of this
data and includes how these
methods would help predict the
use of the data in driving
Justifies why the methods
chosen are the most
appropriate for the analysis but
justification is either illogical or
cursory or does not include
how they would help predict
Does not justify why methods
were chosen (0%)
7.66
decisions using cited evidence
(90%)
the use of data in driving
decisions or does not use or
provide cited evidence (70%)
Data-Driven Decisions:
Process
Meets “Proficient” criteria and
offers great detail for each
identified step (100%)
Outlines the process needed to
utilize the statistical analysis
(90%)
Outlines the process needed to
utilize the statistical analysis
but steps are either
inappropriate or
overgeneralized (70%)
Does not outline the process
needed to utilize the statistical
analysis (0%)
7.66
Data-Driven Decisions: Data
Mining
Meets “Proficient” criteria and
explanation demonstrates a
nuanced understanding of how
following a data mining process
will lead to a solution(100%)
Explains how data mining is
used to develop a solution to
the case, considering concepts
of metadata (90%)
Explains how data mining is
used to develop a solution to
the case but explanation does
not consider concepts of
metadata, is inappropriate, or
cursory (70%)
Does not offer an explanation
why data mining is used to
develop a solution in the case
(0%)
7.66
Data-Driven Decisions:
Structured vs. Unstructured
Meets “Proficient” criteria and
explanation demonstrates a
Explains if the problem is a
structured or unstructured
Explains if the problem is a
structured or unstructured
Does not explain if the problem
is a structured or unstructured
7.66
nuanced understanding of a
structured versus unstructured
problem (90%)
problem but explanation
contains inaccuracies or is
problem (0%)
problem (100%)
cursory (70%)
Criteria
Exemplary
Proficient
Data-Driven Decisions:
Meets “Proficient” criteria and
Assesses how the variables
Assess how the variables have
Does not assess how the
Variables
assessment is demonstrates a
deep understanding of how the
have potential for answering
the problem (90%)
potential for answering the
problem but assessment
variables have potential for
answering the problem (0%)
variables can address the
problem statement (100%)
Needs Improvement
Not Evident
Value
7.66
contains inaccuracies or is
cursory (70%)
Recommend Operational
Improvements: Data-Driven
Meets “Proficient” criteria and
illustration demonstrates a
Illustrates a data-driven
decision that addresses the
Illustrates a data-driven
decision that addresses the
Does not illustrate a decision
that addresses the problem
Calculation
deep understanding of the
interplay between a problem
given problem statement (90%)
problem statement but
illustration is either
statement (0%)
statement, the operation, and
operational improvement
7.66
inappropriate or
overgeneralized (70%)
(100%)
Recommend Operational:
Meets “Proficient” criteria and
Summarizes results of the
Summarizes results of the
Does not summarize the results
Summary of Results
description demonstrates keen
insight into communicating the
analysis plan and explains the
validity of the model solution
analysis plan but summary is
cursory or contains
(0%)
results of the analysis plan
(100%)
(90%)
inaccuracies or does not explain
the validity of the model
7.66
solution (70%)
Recommend Operational
Meets “Proficient” criteria and
Explains how the model
Explains how the model
Does not explain how the
Improvements: Solution
explanation is exceptionally
clear and contextualized
solution addresses the given
problem statement (90%)
solution addresses the given
problem statement but
model solution addresses the
given problem statement (0%)
(100%)
7.66
explanation is cursory, illogical,
or contains inaccuracies (70%)
Articulation of Response
Submission is free of errors
Submission has no major errors
Submission has major errors
Submission has critical errors
related to citations, grammar,
spelling, syntax, and
related to citations, grammar,
spelling, syntax, or organization
related to citations, grammar,
spelling, syntax, or organization
related to citations, grammar,
spelling, syntax, or organization
organization and is presented
in a professional and easy to
(90%)
that negatively impact
readability and articulation of
that prevent understanding of
ideas (0%)
read format (100%)
4.04
main ideas (70%)
Total:
100%
1
QSO 510 Milestone One
Student’s Name
Academic Institution
Instructor
Course
December 26, 2023
2
I.
Introduction to the problem:
The Paralyzed Veterans of America (PVA), a charity organization that represents the
interest of disabled veterans by supporting their healthcare, and research and education in spinal
cord injury and diseases. PVA relies on the generous donations from people who are passionate
about supporting the well-being of veterans. The organization has been soliciting donations by
sending out free greeting cards and mailing address labels to potential donors and requesting
donations. While it has been successful at this approach, PVA would like to save up to $20
million a year and waste paper by half by only sending gift cards to potential donors with a high
likelihood of response.
II.
Analysis
A. Quantifiable Factors
In order to estimate variables with the highest quantifiable effect on GIFTAMNT, PVA
should focus on sending gift cards and mailing address labels to groups associated with the
variables such as the Amount of the recent gift (LASTGIFT), Average amount of gifts to date in
$ (AVGGIFT), Number of card promotions received in last 12 months (NUMPRIM12), Amount
of smallest gift to date in $ (MINRAMNT), Marketing Cluster Code – nominal field
(CLUSTER2), and HOMEOWNER (0=No, 1=Yes). These variables have quantifiable effects on
the GIFTAMNT and it is important that PVA considers the recommendations that follow
analysis.
B. Problem Statement
PVA can save costs of up to $20 million and reduce wasted paper by half by only sending
gift cards and mailing address labels to specific groups of potential donors. To develop a
3
strategy for this problem statement, a multiple regression model, a statistical technique, was used
to come up with a suitable solicitation approach. The model is efficient when analyzing the
relationship between a single response variable and several independent variables, which is the
case with PVA (Sharpe et al., 2021).
The summary descriptive statistics for GIFTAMNT is show in table 1: The mean
GIFTAMNT is $15.72 with a standard deviation of 12.03. The maximum GIFTAMNT is $200
and minimum GIFTAMNT is $1.
Table 1: Measures of central tendency
Column
Mean
n
GIFTAMNT
15.716623 3648
Std. dev.
Std. err.
Variance
Min Max Mode
12.02915
0.19916248
144.70045
1 200
10
GIFTAMNT is positively skewed and asymmetrical in shape as shown in the histogram
below. This is demonstrated with mean and mean of the GIFTAMNT not falling at the center
(not equal).
4
The multiple regression model was performed to determine how the selected different
independent variables i.e. LASTGIFT, AVGGIFT, NUMPRIM12, MINRAMNT, CLUSTER2,
and HOMEOWNER relate to the response variable (GIFTAMNT as provided in table 2.
Table 2: Multiple Regression Model Estimates
Parameter
Estimate
Std. Err.
DF
T-Stat
P-value
Intercept
5.0066903
0.54153895
3636
9.2453004
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