Question Week 1 discussion DQ1 Present an example of a business situation th

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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