Reference no: EM131911879
Assignment
This assignment includes 4 calculation questions regarding Chapter 4 Forecasting.
1. The following gives the number of pints of type B 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 a = .2.
2. As you can see in the following table, demand for heart transplant surgery at Washington General Hospital has increased steadily in the past few years:
|
Year
|
1
|
2
|
3
|
4
|
5
|
6
|
|
Heart Transplants
|
45
|
50
|
52
|
56
|
58
|
?
|
The director of medical services predicted 6 years ago that demand in year 1 would be 41 surgeries.
a) Use a 3-year moving average to forecast demand in years 4, 5, and 6.
b) Use the trend-projection method to forecast demand in years 1 through 6.
c) With MAD as the criterion, which forecasting method is better?
3. George Kyparisis owns a company that manufactures sailboats. Actual demand for George's sailboats during each of the past four seasons was as follows:
|
Season
|
1
|
2
|
3
|
4
|
|
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
|
George has forecasted that annual demand for his sailboats in year 5 will equal 5,600 sailboats. Based on this data and the multiplicative seasonal model, what will the demand level be for George's sailboats in the spring of year 5?
4. The number of auto accidents in Athens, Ohio, is related to the regional number of registered automobiles in thou- sands (X1), alcoholic beverage sales in $10,000s (X2), and rainfall in inches (X3). Furthermore, the regression formula has been calculated as:
Y=a+b1X1 +b2X2 +b3X3
Where Y = number of automobile accidents
a = 7.5
b1 = 3.5
b2 = 4.5
b3 = 2.5
Calculate the expected number of automobile accidents under conditions a, b, and c:
|
|
X1
|
X2
|
X3
|
|
(a)
|
2
|
3
|
0
|
|
(b)
|
3
|
5
|
1
|
|
(c)
|
4
|
7
|
2
|