Southern New Hampshire University Decision Making Analysis What economic theories and quantitative techniques are used to solve business decision problems

Southern New Hampshire University Decision Making Analysis What economic theories and quantitative techniques are used to solve business decision problems and how are they applicable in real-world settings? ECO 301: Decision-Making Analysis Paper Guidelines and Rubric
Overview
Your final project for this course is a detailed analysis of a specific problem statement. How economic themes, such as demand, production, cost, and market
structure relate to a particular company will be a focus of this analysis. You will analyze these components with quantitative techniques, like regression
analysis and linear programming. You will select a product or service with substantial data. Some possible topics: unemployment and crime, exports and
underdeveloped countries, demand/supply of higher education, air pollution and population, etc. The final deliverable will include an introduction, problem
statement, listing of data sources collected, estimate and analyses of data, and a conclusion addressing how your findings can inform future real-world decision
making, at both an organizational level and an individual level.
The project is divided into three milestones and a final product, which will be submitted at various points throughout the course to scaffold learning and ensure
quality final submissions.
Prompt
Your final project should answer the following prompt: What economic theories and quantitative techniques are used to solve business decision problems and
how are they applicable in real-world settings?
Specifically, the following critical elements must be addressed:
1.
Introduction/Statement of Problem
A. What problem are you trying to solve? Discuss the history and key information about the problem and address why the issue/problem is
important.
B. Describe the model, hypothesis, and theoretical framework that will be used to explain and forecast variables. The model should be in the form of
functional equations. QD = f(P, Y, …)
C. What data sources do you plan to use? A minimum sample size of 15 is required. Include a complete description of the data sources and assess
their validity, accuracy, creditability, and reliability for the chosen issue. Make sure all data sources are referenced.
D. Which variables are used in the model? Why are they used? Considering the relation among measurable variables, what is the impact of an
independent variable X on a dependent variable Y? Are there additional independent variables that could influence variable Y? If so, explain.
E. What assumptions can you make about the data? In your analysis, consider the following: accuracy, consistence, sample as representative of the
population, biased/unbiased, efficient, and weakness of data (currency, not a complete data set, biased, not scientifically accurate).
F. What estimation procedure do you plan to use? If you are using time series data, be sure to account for the identification problem. Why did you
choose is this particular procedure?
II.
Regression Analysis – Perform a regression analysis on the data.
A. Compute for the following:
1. Standard errors
2. T-stats
3. R2
4. Coefficients, 5% and 1% levels
5. Possible other models to test
III.
Computer Output and Evaluation of Results – What are the results of your regression analysis? Include the data you used to estimate the demand
model, the results of your analysis, an analysis of the results, and a description of remaining issues that need to be addressed, i.e. other models to test.
Be sure to include standard errors, t-stats, R2, coefficients, 5% and 1% levels.
IV.
Statistical Analysis of Results – What are the implications of the t-stats, F test and Adjusted R2?
be contradictory, how can this be resolved?
Are they consistent or contradictory? If they seem to
V.
Conclusion and General Comments – What are key take-a-ways that can be applied in your own personal or professional real-world settings? Provide
concrete or hypothetical examples (if you are not in an applicable field) that support your conclusions. How might this model change in the future
given assumptions?
Deliverables
Milestone
Deliverables
Due in Module
Grading Metric
1
Two
Regression Analysis
2
Three
Demand Analysis
3
Final Project Milestone One: Regression Analysis Practice
Submit your sample regression analysis and statistical test
Final Project Milestone Two: Demand Analysis
Submit your demand analysis
Final Project Milestone Three: Regression Analysis
Submit your project-based regression analysis
Decision Making Analysis Paper
Submit the final version of all portions of your final paper within
a single document.
Five
Regression Analysis
Seven
Final Project Rubric
Final
Submission
Rubrics
Requirements of submission: Written components of projects must follow these formatting guidelines when applicable: double spacing, 12-point Times New
Roman font, 1-inch margins, and discipline-appropriate citations.
Milestone One and Three: Regression Analysis
Value
90
Critical Elements
Milestone
Submission:
Regression Analysis
Exemplary(100%)
Meets “Proficient” criteria and
includes detailed analysis of
why the computations were
chosen or other appropriate
models were selected
Proficient(85%)
Needs Improvement(55%)
Not Evident (0%)
Performs a regression analysis, Performs a regression analysis, Does not perform a regression
computing standard errors, t- computing standard errors, t- analysis
stats, R2, coefficients (5% and stats, R2, coefficients (5% and
1%), and other possible models 1%), or other possible models,
correctly
but not all models are used or
there are incorrect
computations
Submission has no major errors Submission has major errors Submission has critical errors
related to citations, grammar, related to citations, grammar, related to citations, grammar,
spelling, syntax, or organization spelling, syntax, or organization spelling, syntax, or organization
that negatively impact
that prevent understanding of
readability and articulation of ideas
main ideas
Total
10
Articulation of
Response
Submission is free of errors
related to citations, grammar,
spelling, syntax, and
organization and is presented
in a professional and easy-to-
read format
100%
Milestone Two: Demand Analysis
Critical Elements
Exemplary(100%)
Proficient(85%)
Needs Improvement(55%)
Not Evident (0%)
Introduction and Meets “Proficient” criteria and Describes selected problem or Describes selected problem or Does not describe selected
Statement of Problem selection of problem is service and includes background service and includes background problem or service or does not
supported by scholarly research information on why the issue or information, but misses why the provide background information
problem is important
issue or problem is important
Value
20
Model Formation
20
Meets “Proficient” criteria and
description is insightful and
meticulously detailed
Accurately describes the model, Describes the model, hypothesis, Does not describe the model,
hypothesis, and theoretical and theoretical framework that hypothesis, and theoretical
framework that will be used to will be used to explain and framework that will be used to
explain and forecast variables in forecast variables, but there are explain and forecast variables
the form of a functional equation inaccuracies or description is
lacking in detail
Data Sources
10
Meets “Proficient” criteria and
data sources selection is
accurately justified
Does not describe data sources
to be used
Identifies data sources to be
used, including reference
information, and addresses the
validity, accuracy, creditability,
and reliability of the data
Identifies data sources to be
used, but does not include
reference information or does
not address the validity,
accuracy, creditability, and
reliability of the data
20
Data Sources:
Variables
Meets “Proficient” and includes Describes and explains why Describes and explains why Does not describe or explain
minimum qualitative sample size variables used are chosen, variables used is chosen, but variables chosen
of 15.
including how they fit into the with gaps in how they fit into the
model. Considers the relation model. Considers the relation
among measurable variables and among measurable variables but
analyzes the impact of both missing the impact of both
independent and dependent independent and dependent
variables.
variables.
20
Data Sources: Meets “Proficient” criteria and Analysis of data considers Analysis of data does not Does not describe aspects of
Assumptions of compares data to other sources accuracy, consistency, is consider accuracy, consistency, is organizational culture that
Data/Weaknesses of for accuracy, consideration of representative of the population representative of the population have resulted from the leader’s
Data trends, resource and technical and unbiased.
and unbiased.
decisions and values
constraints.
10
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,
related to citations, grammar,
related to citations, grammar,
related to citations, grammar,
spelling, syntax, and organization spelling, syntax, or organization spelling, syntax, or organization spelling, syntax, or organization
and is presented in a
that negatively impact
that prevent understanding of
professional and easy-to-read
readability and articulation of ideas
format
main ideas
100%
Requirements of submission: The final project submission should be approximately 6-8 pages, double spacing, 12-point Times New Roman font, 1-inch margins,
and discipline-appropriate citations.
Value
5
Critical Elements
Exemplary(100%)
Proficient(85%)
Introduction/ Meets “Proficient criteria and Describes selected problem or
Statement of Problem: selection of problem is service and includes background
History and Key supported by scholarly information on why the issue or
Information research
problem is important
Needs Improvement(55%)
Not Evident (0%)
Describes selected problem or service Does not describe selected
and includes background information, problem or service or does not
but misses why the issue or problem is provide background
important
information
15
Introduction/ Meets “Proficient criteria and Accurately describes the model,
Statement of Problem: description is insightful and hypothesis, and theoretical
Model meticulously detailed
framework that will be used to
explain and forecast variables in
the form of a functional equation
Describes the model, hypothesis, and Does not describe the model,
theoretical framework that will be used hypothesis, and theoretical
to explain and forecast variables, but framework that will be used to
there are inaccuracies or description is explain and forecast variables
lacking in detail
10
Introduction/ Meets “Proficient” criteria and Identifies data sources to be used, identifies data sources to be used, but Does not describe data
Statement of Problem: data sources selection is including reference information, does not include reference information sources to be used
Data Sources accurately justified
and addresses the validity, or does not address the validity,
accuracy, creditability, and accuracy, creditability, and reliability of
reliability of the data
the data
10
Introduction/ Meets “Proficient” criteria and Describes and explains why
Statement of Problem: analysis is supports accurate variables used are chosen,
Variables results
including how they fit into the
model. Considers the relation
among measurable variables and
analyzes the impact of both
independent and dependent
variables
Describes and explains why variables Does not describe and explains
used are chosen, including how they fit why variables used are chosen
into the model, but does not consider and how they fit into the
the relation among measurable model
variables or explanation is missing the
impact of both independent and
dependent variables
10
Introduction/ Meets “Proficient” criteria and Analysis of data considers accuracy, Analysis of data does not adequately
Statement of Problem: compares data to other consistency, representation of the address all key factors of accuracy,
Assumptions sources for accuracy,
population, bias, efficiency and consistency, representation of the
consideration of trends, weakness of data
population, bias, efficiency and
resource and technical
weakness of data
constraints.
Does not attempt to analyze
any of the key factors of
accuracy, consistency,
representation of the
population, bias, efficiency
and weakness of data

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