Forecasting with Indices
Andrea
Quantitative Reasoning for Business - QRB 501
Kumar Das, PhD
January 24, 2011 Forecasting is a tool business use to help plan for the future. Businesses forecast, revenue, overhead, needed inventory, consumers demands, as well as other factors. Forecasting helps to determine the future market value of a company or organization. Forecasting is important because the curse of action business take today often depends on what is going to happen in the future.
The winter historical data for the University of Phoenix covers four years of demand in actual units. Each year has been broken down to 12 months of data. The data is presented in figure 1.1 Typical Seasonal Demand for Winter Highs Actual Demands (in units) Month Year 1 Year 2 Year 3 Year 4 Forecast
1 55,200 39,800 32,180 62,300
2 57,350 64,100 38,600 66,500
3 15,400 47,600 25,020 31,400
4 27,700 43,050 51,300 36,500
5 21,400 39,300 31,790 16,800
6 17,100 10,300 31,100 18,900
7 18,000 45,100 59,800 35,500
8 19,800 46,530 30,740 51,250
9 15,700 22,100 47,800 34,400
10 53,600 41,350 73,890 68,000
11 83,200 46,000 60,200 68,100
12 72,900 41,800 55,200 61,100
Avg.
Figure 1.1 (University of Phoenix, 2011).
The Time-Series Method is an example of quantitative forecasting. This forecasting model uses historical data to try to predict future events .Knowing the data from the last four years, will help to predict how long the demand in units bases on the past (Encyclopedia of Business and Finance, 2006).
The data has been used in creating a scatter chart. Figure 1.2 There is a 64.8% increase in the average units from year one to year two, a 10.3% increase from year two to year three, and a 2.4% increase from year three to year four. As the percentage of growth has increased at a lesser percent each year, it follows that the percentage in increase from year four to year five would decrease exponentially to the decreases in the years prior. Thus the projected increase from year four to year five is 1.1% and with a respective correlation between years of:
Correlation between year 1 and 2 0.3926
Correlation between year 2 and 3 0.0973
Correlation between year 3 and 4 0.4664
Correlation between year 1 and 4 0.8424
Predicted Correlation year 1 and 5 0.9186

Thus the averages would be as follows.
Year 1 2 3 4 5
Average 38,113 40,586 44,802 45,896
Forecast 50,486

The projected data is:
Month Year 1 Year 2 Year 3 Year 4 Forecast
1 55,200 39,800 32,180 62,300 74,300
2 57,350 64,100 38,600 66,500 96,500
3 15,400 47,600 25,020 31,400 48,500
4 27,700 43,050 51,300 36,500 31,240
5 21,400 39,300 31,790 16,800 10,800
6 17,100 10,300 31,100 18,900 12,900
7 18,000 45,100 59,800 35,500 25,600
8 19,800 46,530 30,740 51,250 63,125
9 15,700 22,100 47,800 34,400 27,500
10 53,600 41,350 73,890 68,000 59,015
11 83,200 46,000 60,200 68,100 78,200
12 72,900 41,800 55,200 61,100 78,150
Avg. 38,113 40,586 44,802 45,896 50,486 64.80% 10.30% 2.40% 1.10% percentage of increase
Figure 1.3
The data reflected in a scatter chart As has been shown above there is a projected increase in demand per unit of 1.1% for an average of 50,486 units. This information allows the University of Phoenix to schedule and budget accordingly. This projected data is helpful in controlling cost and in turn helping the University of Phoenix to meet the demands without waste, thus increasing profits.

Reference
Winter Historical Data, 2011 Retrieved January 18 from https://portal.phoenix.edu/classroom/coursematerials/qrb_501/20110104/
Forecasting in Business (2006). Encyclopedia of Business and Finance. Ed. Allison McClintic Marion. Gale Cengage, 2001. eNotes.com. Retrieved Jan 21, 2011 from http://www.enotes.com/business-finance-encyclopedia/
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