blood used at Woodlawn Hospital in the past 6 weeks:
Week Of Pints Used
August 31 360
September 7 389
September 14 410
September 21 381
September 28 368
October 5 374
a) Forecast the demand for the week of October 12 using a 3-week moving average.
b) Use a 3-week weighted moving average, with weights of .1, .3, and .6, using .6 for the most recent week. Forecast demand for the week of October 12.
c) Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 and = .2.
4.3 Refer to Problem 4.2. Develop a forecast for years 2 through 12 using exponential smoothing with = .4 and a forecast for year 1 of 6. Plot your new forecast on a graph with the actual data and the naive forecast. Based on a visual inspection, which forecast is better
Year 1 2 3 4 5 6 7 8 9 10 11
Demand 7 9 5 9 13 8 12 13 9 11 7
4.5 The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the anticipated mileage to be driven next year. The miles driven during the past 5 years are as follows:
Year Mileage
1 3,000
2 4,000
3 3,400
4 3,800
5 3,700
a) Forecast the mileage for next year using a 2-year moving average.
b) Find the MAD based on the 2-year moving average forecast in part (a). (Hint: You will have only 3 years of matched data.)
c) Use a weighted 2-year moving average with weights of .4 and .6 to forecast next year’s mileage. (The weight of .6 is for the most recent year.) What MAD results from using this approach to forecasting? (Hint: You will have only 3 years of matched data.)
d) Compute the forecast for year 6 using exponential smoothing, an initial forecast for year 1 of 3,000 miles, and = .5.
4.25 The following gives the number of accidents that
occurred on Florida State Highway 101 during the last 4 months:
Month Number of Accidents
January 30
February 40
March 60
April 90
Forecast the number of accidents that will occur in May, using least squares regression to derive a trend equation.
4.27 Mark Cotteleer owns a company that manufactures sailboats. Actual demand for Mark’s sailboats during each season in 2004 through 2007 was as follows:
Year
Season 2004 2005 2006 2007
Winter 1,400 1,200 1,000 900
Spring 1,500 1,400 1,600 1,500
Summer 1,000 2,100 2,000 1,900
Fall 600 750 650 500
Mark has forecasted that annual demand for his sailboats in 2009 will equal 5,600 sailboats. Based on this data and the multiplicative seasonal model, what will the demand level be for Mark’s sailboats in the spring of 2009?
4.33 The number of transistors (in millions) made at a plant in Japan during the past 5 years follows:
Year Transistors
1 140
2 160
3 190
4 200
5 210
a) Forecast the number of transistors to be made next year, using linear regression.
b) Compute the mean squared error (MSE) when using linear regression.
c) Compute the mean absolute percent error (MAPE).
