A toy company buys large quantities of plastic pellets for use in the manufactur
ID: 406980 • Letter: A
Question
A toy company buys large quantities of plastic pellets for use in the manufacturing of its products. The production manager wants to develop a forecasting system for plastic pellet prices and is considering four different approaches and 6 different models. He plans to use historical data to test the different models for accuracy. The price per pound of plastic pellets (actual) has varied as shown:
Month
Price/Pound
1
$0.39
2
0.41
3
0.45
4
0.44
5
0.40
6
0.41
7
0.38
8
0.36
9
0.35
10
0.38
11
0.39
12
0.43
13
0.37
14
0.38
15
0.36
16
0.39
SIMPLE MOVING AVERAGE
Using an averaging period of 3, calculate the simple moving average for months 7-16. Calculate the MAD for this forecasting model.
Using an averaging period of 4, calculate the simple moving average for months 7-16. Calculate the MAD for this forecasting model.
WEIGHTED MOVING AVERAGE
Using an averaging period of 3 with the most recent period weighted .5, the next most recent .3, and the oldest period weighted .2, forecast months 7-16. Calculate the MAD for this forecasting model.
EXPONENTIAL SMOOTHING
Compute the forecasts for months 7-16 using = .1. Assume that the forecast for month 1 is .39. You will need to forecast months 2-6 to get to the starting forecast for month 7, however, only include months 7-16 in the calculation of the MAD.
Compute the forecasts for months 7-16 using = .3. Assume that the forecast for month 1 is .39. You will need to forecast months 2-6 to get to the starting forecast for month 7, however, only include months 7-16 in the calculation of the MAD.
SIMPLE LINEAR REGRESSION
Use all of the data, months 1-16, to calculate the regression equation for this data. Use the months (1-16) as the independent variable (x) and price as the dependent variable (y). Once you have the equation, forecast months 7-16 (enter 7, 8, etc. as the x value in the equation). Calculate the MAD for this forecasting model. Comment on the goodness of fit (R2) and significance of the model (F significance) to determine if this forecast model should be included in the consideration of the different approaches. (If the model is not significant, it cannot be considered, however, you must still make the forecasts and calculate the MAD).
Show your work in a table like the one below
SMA AP=3
SMA AP=4
WMA
ETC
Month
Actual
Forecast
Absolute Deviation
Forecast
Absolute Deviation
Forecast
Absolute Deviation
7
0.38
8
0.36
Month
Price/Pound
1
$0.39
2
0.41
3
0.45
4
0.44
5
0.40
6
0.41
7
0.38
8
0.36
9
0.35
10
0.38
11
0.39
12
0.43
13
0.37
14
0.38
15
0.36
16
0.39
Explanation / Answer
Simpel Average for 3 months
Month ::::::::::::::::::::::::Price ::::::::::::::::::::::::::Forecast::::::::::::::::::::::::Error::::::::Absolute Error:
7:::::::::::::::::::::::::::::::0.38::::::::::::::::::::::::::::::::::0.42::::::::::::::::::::::::-0.04::::::::::-0.04
8:::::::::::::::::::::::::::::::0.36::::::::::::::::::::::::::::::::::0.40::::::::::::::::::::::::-0.04::::::::::-0.04
9:::::::::::::::::::::::::::::::0.35::::::::::::::::::::::::::::::::::0.38::::::::::::::::::::::::-0.03:::::::::-0.03
10:::::::::::::::::::::::::::::0.38::::::::::::::::::::::::::::::::::0.36::::::::::::::::::::::::0.02::::::::0.02
11:::::::::::::::::::::::::::::0.39:::::::::::::::::::::::::::::::::::0.36:::::::::::::::::::::::0.03::::::::0.03:
12::::::::::::::::::::::::::::0.43:::::::::::::::::::::::::::::::::::0.37::::::::::::::::::::::::0.06::::::::0.06
13::::::::::::::::::::::::::::0.37:::::::::::::::::::::::::::::::::::0.40::::::::::::::::::::::::-0.03::::::::-0.03
14:::::::::::::::::::::::::::::0.38:::::::::::::::::::::::::::::::::::0.40::::::::::::::::::::::-0.02::::::::-0.02
15:::::::::::::::::::::::::::::0.36::::::::::::::::::::::::::::::::::::0.39:::::::::::::::::::::-0.03::::::::-0.03
16:::::::::::::::::::::::::::::0.39::::::::::::::::::::::::::::::::::::0.37:::::::::::::::::::::0.02::::::::0.02
__________________________________________________________________________
Simpel Average for 4 months
Month ::::::::::::::::::::::::Price ::::::::::::::::::::::::::Forecast::::::::::::::::::::::::ErrorcAbsolute Error
7 ::::::::::::::::::::::::::::::0.38 :::::::::::::::::::::::::::::::0.42 ::::::::::::::::::::::::::-0.04::::::::-0.04
8 ::::::::::::::::::::::::::::::0.36 :::::::::::::::::::::::::::::0.41 ::::::::::::::::::::::::::-0.05::::::::::-0.05
9 ::::::::::::::::::::::::::::::0.35 ::::::::::::::::::::::::::::::0.39 ::::::::::::::::::::::::::-0.04::::::::-0.04
10 ::::::::::::::::::::::::::::0.38 ::::::::::::::::::::::::::::::0.37 ::::::::::::::::::::::::::0.01::::::::::0.01
11 ::::::::::::::::::::::::::::::0.39 :::::::::::::::::::::::::::::0.36 ::::::::::::::::::::::::::0.03::::::::0.03
12 ::::::::::::::::::::::::::::::0.43 ::::::::::::::::::::::::::::0.37 ::::::::::::::::::::::::::0.06::::::::0.06
13 ::::::::::::::::::::::::::::::0.37 ::::::::::::::::::::::::::0.39 :::::::::::::::::::::::::::-0.02::::::::-0.02
14 ::::::::::::::::::::::::::::::0.38 ::::::::::::::::::::::::::0.39 ::::::::::::::::::::::::::-0.01::::::::-0.01
15 ::::::::::::::::::::::::::::::0.36 ::::::::::::::::::::::::::0.29 ::::::::::::::::::::::::::0.07::::::::0.07
16 ::::::::::::::::::::::::::::::0.39 ::::::::::::::::::::::::::0.48 ::::::::::::::::::::::::::-0.09::::::::-0.09
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