Week 1 discussion
DQ1 Present an example of a business situation that you
believe would lend itself to the use of a quantitative business model. Clearly
explain how the model could be used in this situation.
DQ2 Multiple models are often used in supporting business
decision making. Outline a situation in your organization or industry that
required the need for multiple models. What factors were unique to this
situation? Support your response with rationale from the readings or external
research.
Week 2 discussion
DQ1 Apply a business decision model to something you do
every day, such as select an outfit, order lunch, or determine your exercise
routine. Be creative in your approach. How did you select the model? Include
rationale with support from the readings.
DQ2 Using the decision tree resources available in the Topic
Materials, create a decision tree for the scenario you outlined in Topic 2 DQ
1. Attach the decision tree to your response and include insights in crafting
the decision tree. How would you apply your experience to larger-scale
decisions at an organizational level?
Week 3 discussion
DQ1 Identify two business situations or problems within your
current organization or industry. Articulate how one of these lends itself to a
simple linear regression and how one does not. Why is simple linear regression
appropriate to address one situation or problem but not the other? Support your
ideas with evidence from the readings.
DQ2 You are the vice president of sales for TerraFirma, a
company that manufactures outdoor sporting gear. You receive a report on your
desk one morning that claims little or no relationship between the University
of Michigan Consumer Sentiment Index (CSI) and outdoor sporting gear sales. The
claim is based on a very low R2 of the simple regression model, using these two
variables (CSI and sales). Discuss how you would (or should) react to this
report and why. What clarifying questions might you ask?
Week 4 discussion
DQ1 Provide an example based on your professional experience
of a situation in which using a multiple regression model or nonlinear
regression model may have helped your organization make a better decision.
DQ2 What types of business situations or problems might best
lend themselves to multiple linear regression? What types may not? When do you
anticipate using a multiple linear regression model in your postgraduate,
professional experience? Explain.
Week 5 discussion
DQ1 Discuss the strategic importance of forecasting at your
organization (or one with which you are familiar). What strategic decisions
does it need to make in terms of forecasting? Provide two recent examples. In
your opinion, was this the best way? How could the process be improved?
DQ2 Refer to the Topic Material, “Chapter 1 –
Fundamental Issues in Business Forecasting.” This resource includes a
discussion of unrealistic expectations and forecast accuracy. How have you seen
this demonstrated in your organization or industry? Describe the forecasting
scenario and the “prediction” that did not come true. What
conversations did management have surrounding this issue? How would you
mitigate expectations for a situation like this in the future?
Week 6 discussion
DQ1 Identify two key strategic decisions made by your
current team, department, or organization. How could those decisions have been
enhanced by optimization models? Support your rationale with evidence from
readings or external research.
DQ2 Find a current example of a linear optimization model
used in your industry. Describe the industry’s needs, including any unique
factors, how the linear optimization model was used, and the problem or
challenge it addressed. Would you suggest a different model be used? Why or why
not? Support your response with rationale from the assigned readings.
Week 7 discussion
DQ1 Explain the importance of correctly stating the
objective function and constraints in linear optimization problems. Using
examples from your professional experience, describe the problems that could
result if the objective function and constraints are not stated properly. Why
did these problems arise? Support your anecdotal evidence with support and
rationale from the readings.
DQ2 Describe a workforce scheduling, a blending, and a
logistics problem facing your current organization or industry. What is being
optimized in each of your examples and why? How do linear optimization
techniques differ from decision tree analysis? Which are more applicable to the
examples you identified? Support your response with rationale from the
readings.
Week 8 discussion
DQ1 Describe a current problem facing your department,
organization, or industry that would indicate the need for simulation. What key
factors of this business situation indicate the need for simulation (versus the
other modeling techniques covered in the course)? Support your response with
rationale from the readings.
DQ2 Consider some of the examples you have brought up in
earlier topics. Describe the key differences between simulation models and the
models covered earlier in the course. Outline how the approach to solving this
problem would differ in terms of applying and computing/solving the models.
Week 1
assignment
Ethical Decision-Making Essay
Throughout this course, you will participate in a variety of
critical thinking exercises designed to engage you in evaluating and selecting
appropriate quantitative models and methods. A key aspect of this process
involves ethical considerations. In an essay of 750-1,000 words, explore
ethical decision making and arrive at conclusions relevant to your industry and
perspectives of Christian worldview.
How do ethical business practices influence the evaluation,
selection, and application of an analytical, quantitative business model? How
does the selection of an appropriate business model reflect ethical practice?
Frame your ethical considerations from both a Christian worldview and business
practice perspective.
What role do individuals and management play in ensuring the
appropriate business model is chosen, used, and evaluated for effectiveness?
Support your assertions with evidence from the readings,
external research, and the textbook.
Prepare the assignment according to the guidelines found in
the APA Style Guide, located in the Student Success Center. An abstract is not
required.
This assignment uses a rubric. Please review the rubric
prior to beginning the assignment to become familiar with the expectations for
successful completion.
You are required to submit this assignment to Turnitin.
Please refer to the directions in the Student Success Center.
Week 2
assignment
Decision Analysis Case Study: Valley of the Sun Reviews
For many of the remaining topics in BUS-660, assignments
will be in the form of case studies. These case studies are designed to provide
an opportunity to engage in that topic’s quantitative analysis method, as well
as demonstrate critical thinking and appropriate professional communication.
Review “Decision Analysis Case Study: Valley of the Sun
Reviews” for this topic’s case study, a proposal to change the faculty
performance review process at Valley of the Sun Academy (VSA).
Based on the information presented in the case study, create
a decision tree or Excel-based analysis to determine the most appropriate
recommendation.
In a 500-750-word report to VSA’s Human Resources department
and the chief financial officer, explain your approach and the rationale for
this method. Evaluate both outcomes and how they would be applied to this
decision. Conclude your report with your recommendation for the review process
VSA should adopt.
Submit your Excel-based analysis or decision tree with your
report.
Prepare the assignment according to the guidelines found in
the APA Style Guide, located in the Student Success Center. An abstract is not
required.
This assignment uses a rubric. Please review the rubric
prior to beginning the assignment to become familiar with the expectations for
successful completion.
You are required to submit this assignment to Turnitin.
Please refer to the directions in the Student Success Center.
Decision Analysis Case Study:
Valley of the Sun Reviews
Valley of the Sun Academy (VSA) is an online school
specializing in GED programs for the Phoenix area. Valley of the Sun Academy
enrolls 813 students and has a part-time faculty pool of 65 online instructors.
Online faculty are reviewed annually and provided with
feedback about their facilitation techniques, content expertise, engagement,
and classroom management. If necessary, remediation and additional support are
provided by the Faculty Advisory Board (FAB). The online faculty reviews are
one factor used to determine overall performance, teaching status, and
potential performance appraisals.
Recently, the FAB submitted a proposal for a new approach
for the next fiscal year, the Peer Faculty Performance Review (PFPR). Human
Resources (HR) and the school’s chief financial officer are evaluating the
suggestion against the current design, described by VSA’s director. Both review
processes are outlined below.
Current Design
Valley of the Sun Academy uses an external firm, TeachBest
Consulting, to conduct annual reviews for online faculty. The review team is
composed of faculty members at other online institutions, including
universities and high schools. Valley of the Sun Academy faculty are not part
of the review process, and TeachBest Consulting handles hiring and training
internally. Valley of the Sun Academy’s HR department assigns completed courses
to review, and VSA’s Technical Support team is responsible for providing
access.
Once completed, the TeachBest consultant submits the review
form to VSA’s HR department, and HR submits a payment for each review. In
addition, VSA has an annual contract with TeachBest Consulting.
The overall contract is $2,500/year. If VSA’s enrollment
reaches 1,000 or more students or their faculty pool expands to 75 or more
instructors, the contract amount will increase to $5,000/year. There is a 75%
chance the student enrollment will reach 1,000 students within the next 18
months and a 25% chance enrollment will not increase. During the next nine
months, Human Resources anticipates hiring at least six math instructors.
Individual reviewers are paid $75 for each review. Reviews
are conducted in March, July, and November, with all faculty reviewed by
December 1.
Valley of the Sun Academy is responsible for disseminating
the results of the review to faculty members. If questions arise about review
results, the FAB is responsible for verifying the review and responding to the
instructor. Periodically, the Faculty Advisory Board finds fault with the
initial review and follow-up must be scheduled. Each year, about 5% of the
initial reviews are found to be inaccurate and new reviews must be scheduled.
Valley of the Sun Academy pays a discounted price of $50 for each follow-up
review.
Peer Faculty Performance Review (PFPR) Proposal
The FAB proposes to conduct faculty reviews in-house and no
longer contract TeachBest Consulting. Human Resources will review faculty files
and invite the top three performing instructors in four disciplines (Literacy
and Communication, Social Sciences, Math, and Science and Technology) to join
the PFPR committee.
Initial responsibilities will involve creating a new review
form and conducting a norming session for consistency. There will be ongoing
technology fees of $20/month for each reviewer, to ensure access to create and
complete the review forms. There will also be an initial cost to set up the
norming session. The Faculty Advisory Board recommends one of three options:
1. A $500
session that can be scheduled at any time with TeachBest Consulting.
2. A $750
session offered monthly by an external employee development firm.
3. A session
designed by VSA’s HR and instructional design specialists, which would be free
to attend but would require internal time and labor costs; HR anticipates a
start of two months from implementation would prevent interrupting normal
business practices.
Because the responsibilities are not included in current
faculty contracts, FAB recommends stipends of $50 for each review completed.
With the new internal PFPR process, FAB anticipates faculty reviews would no
longer be overturned and there would not be a need to conduct secondary
reviews. Additionally, FAB expects reviews to move to a 9-month rolling cycle
rather than once every academic year.
Week 3
assignment
Simple Regression Models Case Study: Mystery Shoppers
Review “Simple Regression Models Case Study: Mystery
Shoppers” for this topic’s case study, a request to evaluate consignment
stores from mystery shopper data.
Based on the information presented in the case study, create
a regression model to determine the most appropriate recommendation.
Prepare a 250-500-word response to Mrs. Turner’s questions
about predicting final scores, statistical significance, and whether a store
location should be closed based on the data provided. Explain your approach and
the rationale for this method. Evaluate the outcomes of your regression model
and the responses to Mrs. Turner’s questions.
Submit a copy of the Excel spreadsheet file you used to
design your regression model and to determine statistical significance.
Note: Students should use Excel’s regression option to
perform the regression.
Use an Excel spreadsheet file for the calculations and
explanations. Cells should contain the formulas (i.e., if a formula was used to
calculate the entry in that cell).
Mac users can use StatPlus:mac LE, free of charge, from
AnalystSoft.
StatPlus:mac LE can be used with Excel 2011 to perform
statistical functions.
Go to the AnalystSoft website and follow the installation
instructions: http://www.analystsoft.com/en/products/statplusmacle/.
Once installed, Apple users can use StatPlus:mac LE to
complete homework problems that require the use of Excel’s data analysis
statistical functions.
Prepare the written portion of this assignment according to
the guidelines found in the APA Style Guide, located in the Student Success
Center. An abstract is not required.
This assignment uses a rubric. Please review the rubric
prior to beginning the assignment to become familiar with the expectations for
successful completion.
You are not required to submit this assignment to Turnitin.
Simple Regression Models Case Study: Mystery Shoppers
Chic Sales is a high-end consignment store with several
locations in the metro area. The company noticed a decrease in sales over the
last fiscal year. Research indicated customer satisfaction had decreased and
the owner, Pat Turner, decided to create a mystery shopper program.
The mystery shopper program lasted over a 6-month period,
employing several loyal and new customers assigned to each location. Surveys
were on a 100-point scale and involved categories such as “Staff Attitude,”
“Store Cleanliness,” “Product Availability,” and “Display(s) Appeal.”
After the mystery shopper period concludes, Mrs. Turner
sends you the following e-mail:
From: Pat Turner
Sent: Thursday, July 7, 2016 8:57 a.m.
Subject: Mystery Data Shopper Stats and Store Performance?
Good morning! Welcome back from vacation ? I hope you had a
wonderful Fourth of July.
The last mystery shopper surveys came in and I have the
final numbers. I am interested in whether there is a way to predict the final
average based on the initial survey score. Also, is there a statistically
significant relationship between how stores initially performed and what the
overall average is?
The initial survey score and the final average data for all
seven store locations is in the table below:
Store 1 2 3 4 5 6 7
Initial Survey Score 83 97 84 72 85 64 93
Final Average 78 98 92 75 88 70 93
Also, how good is the relationship between Initial Survey
Score and the Final Average? Could I use
an Initial Survey Score to predict a Final Average? In fact, could I predict a Final Average if I
have an Initial Survey Score of 90?
If you could have this to me before the weekend, that would
be great.
Thanks so much!
Pat Turner, Owner
Chic Sales Consignment, LLC
Week 4
assignment
Multiple Regression Models Case Study: Web Video on Demand
Review “Multiple Regression Models Case Study: Web
Video on Demand” for this topic’s case study, predicting advertising sales
for an Internet video-on-demand streaming service.
After developing Regression Model A and Regression Model B,
prepare a 250-500-word executive summary of your findings. Explain your
approach and evaluate the outcomes of your regression models.
Submit a copy of the Excel spreadsheet file you used to
design your regression model and to determine statistical significance.
Note: Students should use Excel’s regression option to
perform the regression.
Use an Excel spreadsheet file for the calculations and
explanations. Cells should contain the formulas (i.e., if a formula was used to
calculate the entry in that cell). Students are highly encouraged to use the
“Multiple Regression Dataset” Excel resource to complete this
assignment.
Mac users can use StatPlus:mac LE, free of charge, from
AnalystSoft.
Prepare the written portion of this assignment according to
the guidelines found in the APA Style Guide, located in the Student Success
Center. An abstract is not required.
This assignment uses a rubric. Please review the rubric
prior to beginning the assignment to become familiar with the expectations for
successful completion.
You are required to submit this assignment to Turnitin.
Please refer to the directions in the Student Success Center.
Multiple Regression Models Case Study: Web Video on Demand
Web Video on Demand (WVOD) is an Internet video-on-demand
streaming service. The company offers a subscription service for $5.99/month,
which includes access to all programming and 30-second commercial intervals.
In the last year, the company has recently begun producing
its own programming, including 30-, 60-, and 120-minute television shows,
specials, and films. Programming has been developed for teen audiences as well
as adults.
The following data represent the amount of money brought in
through advertising sales, the average number of viewers, length of the
program, and the average viewer age per program.
Advertising Sales
($) Average #
of Viewers
(Millions) Length
of Program (Minutes) Average Viewer
Age
(Years)
28,000 10.1 30 30
25,500 11.4 30 25
31,000 19.9 60 30
29,000 13.6 60 38
20,500 12.5 60 20
14,500 3.5 30 15
27,000 15.1 60 24
23,500 3.7 30 17
19,500 4.3 30 19
23,000 12.2 120 45
18,000 5.1 120 19
29,500 15.9 60 28
30,000 16.8 120 31
25,000 8.5 120 58
22,500 9.1 30 43
The WVOD executives are in the process of evaluating a
partnership with several independent filmmakers to fund and distribute socially
conscious and diverse programming. The executives have asked for regression
models to be developed based on specific needs. The three regression model
requests and programming details are included below.
The WVOD executives would like to see a regression model
that predicts the amount of advertising sales based on the number of viewers
and the length of the program. Develop this regression model (“Regression Model
A”). Web Video on Demand would like to acquire a 60-minute documentary special
about social media and bullying. The special is aimed at teen viewers and is
estimated to bring in 3.2 million viewers. Based on the regression model,
predict the advertising sales that could be generated by the special.
The WVOD executives would also like to see a regression
model that predicts the amount of advertising sales based on the number of
viewers, the length of the program, and the average viewer age. Develop this
regression model (“Regression Model B”). Web Video on Demand may acquire a
2-hour film that was a hit with critics and audiences at several international
film festivals. Initial customer surveys indicate that the film could bring in
14.1 viewers and the average viewer age would be 32. Use this information to
predict the advertising sales.
Week 5
assignment
Forecasting Case Study: New Business Planning
Access the “Entrepreneurship and the U.S. Economy”
page of the Bureau of Labor Statistics website
(https://www.bls.gov/bdm/entrepreneurship/entrepreneurship.htm) and complete
this forecasting assignment according to the directions provided in the
“Forecasting Case Study: New Business Planning” resource.
Use an Excel spreadsheet file for the calculations and
explanations. Cells should contain the formulas (if a formula was used to
calculate the entry in that cell). Students are highly encouraged to use the Excel
resource, “Forecasting Template,” to complete this assignment.
Mac users can use StatPlus:mac LE, free of charge, from
AnalystSoft.
Prepare the assignment according to the guidelines found in
the APA Style Guide, located in the Student Success Center. An abstract is not
required.
This assignment uses a rubric. Please review the rubric
prior to beginning the assignment to become familiar with the expectations for
successful completion.
You are not required to submit this assignment to Turnitin.
Forecasting Case Study: New Business Planning
Important Note: Students must access the “Entrepreneurship
and the U.S. Economy” page of the Bureau of Labor Statistics website in order
to complete this assignment.
Scenario
The generation of new business start-up is vital to the
growth of the economy as it builds new jobs and creates new opportunities for
the community. The Bureau of Labor Statistics tracks new business development
and jobs created on the website for the United States Department of Labor. You
have been tasked with forecasting economic growth and decline patterns for new
businesses in the United States.
Forecasting
Access the “Entrepreneurship and the U.S. Economy” page of
the Bureau of Labor Statistics website. Under the “Business establishment
age” heading, the first chart reviews new businesses less than 1 year old
during the March 1994 to March 2015 period. Click on the [Chart data] link
below the chart:
Once the chart data window opens, you will see the number of
establishments that are less than 1 year old for each year during this period:
Using the five most recent years and the “Forecasting
Template” spreadsheet provided, complete the forecasts for the next two
periods and provide updated Totals and Average Bias, median absolute deviation
(MAD), mean squared error (MSE), and mean absolute percentage error (MAPE) for
all four charts. Provide a Summary Page in Excel with a 500-750 word report on
the analysis completed by the forecasting models. Include review of error,
recommendations on the best forecasting model to use, and analysis of the
business trend data for new business startup in the United States.
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Moving averages – 2 period moving average |
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Num pds |
3 |
|
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|
Data |
Forecasts and Error Analysis |
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|
Period |
Demand |
Forecast |
Error |
Absolute |
Squared |
Abs Pct Err |
||
|
Period 1 |
38 |
|||||||
|
Period 2 |
40 |
|||||||
|
Period 3 |
41 |
39 |
2 |
2 |
4 |
04.88% |
||
|
Period 4 |
37 |
40.5 |
-3.5 |
3.5 |
12.25 |
09.46% |
||
|
Period 5 |
45 |
39 |
6 |
6 |
36 |
13.33% |
||
|
Total |
4.5 |
11.5 |
52.25 |
27.67% |
||||
|
Average |
1.5 |
3.833333 |
17.41667 |
09.22% |
before forecast |
|||
|
|
Bias |
MAD |
MSE |
MAPE |
||||
|
|
|
|
|
|
||||
|
Period 6 |
50 |
47.5 |
2.5 |
2.5 |
6.25 |
05.00% |
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|
Period 7 |
44 |
Average |
|
|
|
|
after forecast period 6 |
|
|
|
Bias |
MAD |
MSE |
MAPE |
||||
|
Moving averages – 3 period moving average |
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|
Num pds |
3 |
|
||||||
|
Data |
Forecasts and Error Analysis |
|||||||
|
Period |
Demand |
Forecast |
Error |
Absolute |
Squared |
Abs Pct Err |
||
|
Period 1 |
38 |
|||||||
|
Period 2 |
40 |
|||||||
|
Period 3 |
41 |
|||||||
|
Period 4 |
37 |
39.66667 |
-2.66667 |
2.666667 |
7.111111 |
07.21% |
||
|
Period 5 |
45 |
39.33333 |
5.666667 |
5.666667 |
32.11111 |
12.59% |
||
|
Total |
3 |
8.333333 |
39.22222 |
19.80% |
||||
|
Average |
1.5 |
4.166667 |
19.61111 |
09.90% |
||||
|
|
Bias |
MAD |
MSE |
MAPE |
||||
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|
|
|
|
|
||||
|
Period 6 |
50 |
44 |
6 |
6 |
36 |
12.00% |
||
|
Period 7 |
44 |
Average |
|
|
|
|
after forecast period 6 |
|
|
|
Bias |
MAD |
MSE |
MAPE |
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