Forecasting Problem
POM Software:
For this part of the problem I need to use the POM software:
1. Forecasting.
2. I should select Module->Forecasting->File->New->Least Squares and multiple regression
3. Use the module to solve the Case Study (Southwestern University). this case study, I am are required to build a forecasting model. Assume a linear regression forecasting model and build a model for each of the five games (five models in total) by using the forecasting module of the POM software.
4. Answer the three discussion questions for the case study except the part requiring me to justify the forecasting technique, as linear regression would be used.
Discussion Questions
1. Develop a forecasting model, justifying
…show more content…
Pitterno had wanted dormitories solely for his athletes in the stadium as an additional feature of any expansion.
SWU's president, Dr. Joel Wisner, decided it was time for his vice president of development to forecast when the existing stadium would "max out." The expansion was, in his mind, a given. But Wisner needed to know how long he could wait. He also sought a revenue projection, assuming an average ticket price of $20 in 2006 and a 5% increase each year in future prices.
Southwestern University Football Game Attendance, 2000-2005
2000
GAME
ATTENDEES OPPONENT
1 34,200 Baylor
2 a 39,800 Texas
3 38,200 LSU
4b 26,900 Arkansas
5 35,100 USC
2001
GAME
ATTENDEES OPPONENT
1 36,100 Oklahoma
2a 40,200 Nebraska
3 39,100 UCLA
4b 25,300 Nevada
5 36,200 Ohio State
2002
GAME
ATTENDEES OPPONENT
1 35,900 TCU
2a 46,500 Texas Tech
3 43,100 Alaska
4b 27,900 Arizona
5 39,200 Rice
2003
GAME
ATTENDEES OPPONENT
1 41,900 Arkansas
2a 46,100 Missouri
3 43,900 Florida
4b 30,100 Miami
5 40,500 Duke
2004
GAME
ATTENDEES OPPONENT
1 42,500 Indiana
2a 48,200 North Texas
3 44,200 Texas A&M
4b 33,900 Southern
5 47,800 Oklahoma
2005
GAME
ATTENDEES OPPONENT
1 46,900 LSU
2a 50,100 Texas
3 45,900
4. Based on your analysis in (1) – (3) above, what is your overall conclusion regarding the
Question 5: Which type of variation was critical to resolving the realized revenue case study?
Ken is 63 years old and unmarried. He retired at age 55 when he sold his business, Understock.com. Though Ken is retired, he is still very active.
6. Although you are basically satisfied with the analysis thus far, you are concerned about the
Critically evaluate the assumptions on which your forecasts are based. What developments could alter your results? Is Mr. Cowins correct in his belief that Hampton can repay the loan in December?
4. For the questions 1-3 we found out a couple different cost functions that could be useful for Delta when predicting salaries for 2003 and 2004. The multiple regressions may be most reliable considering it uses multiple variables, but the single R square is better than multiple regressions.
2. Choose one of the research questions from above and consider it in more detail. Based upon the question, what would be a reasonable hypothesis?
P1 – Explain the effects of changes in the economic environment on a selected business.
Scenario 1: You are preparing fifth grade students for the Alabama Writing Assessment. So far, students are struggling. Students are just not interested in writing. How can you peak their interest and improve their writing scores? Will you incorporate Writer's workshop? Two to three paragraphs.
2. What considerations should a corporate executive in industries such as banking, health care, or higher education investigate when deciding on implementing a data-mining program?
What assumptions did Mr. Fischer make when he prepared the forecasts shown in case Exhibits 1 and 2? Were these assumptions reasonable?
1) Describe Plenitude's position in the US market in the early 1996. Why has it apparently been less successful in the US than in France when the French "success" formula was used in the US?
Question 2. Build a financial model to determine if MBUSA should invest in eLearning. Should MBUSA make the investment? What are the key drivers of value?
Self-fulfilling prophecy is described as any expectation, whether it is positive or negative, about a situation or occurrence that influences an individual’s behavior in such a way that it provides reason that the expectation is to be achieved. While I am employed in an educational setting, an educator may possibly give an expectation of a student being disruptive and ill-mannered. I work with behavioral special education students would have a tendency to disrupt class.
The procedure for this model is to collect several periods of history relating to the independent and dependent variables themselves, establish the relationship that minimizes mean squared error of forecast vs actual using linear or non-linear and singular or multiple regression analysis.