4.2
a) Plot the
above data on a graph. Do you observe any trend, cycles, or random variations?
b)
Starting in year 4 and going to year 12, forecast demand using a 3-year moving
average. Plot your forecast on the same graph as the original data.
c) Starting
in year 4 and going to year 12, forecast demand using a 3-year moving average
with weights of .1, .3, and .6, using .6 for the most recent year. Plot this
forecast on the same graph.
4.6
|
MONTH |
SALES |
|
January |
20 |
|
February |
21 |
|
March |
15 |
|
April |
14 |
|
May |
13 |
|
June |
16 |
|
July |
17 |
|
August |
18 |
|
September |
20 |
|
October |
20 |
|
November |
21 |
|
December |
23 |
a) Plot the
monthly sales data.
b) Forecast
January sales using each of the following:
i) Naive
method.
ii) A 3-month moving average.
iii) A 6-month weighted average using .1,
.1, .1, .2, .2, and .3, with the heaviest weights applied to the most recent
months.
iv) Exponential smoothing using an ? = .3
and a September forecast of 18.
v) A trend projection.
c) With the
data given, which method would allow you to forecast next March’s sales?
4.9 Lenovo
uses the ZX-81 chip in some of its laptop computers. The prices for the chip
during the past 12 months were as follows:
a) Use a
2-month moving average on all the data and plot the averages and the prices.
b) Use a
3-month moving average and add the 3-month plot to the graph created in part
(a).
c) Which is
better (using the mean absolute deviation): the 2-month average or the 3-month
average?
d) Compute
the forecasts for each month using exponential smoothing, with an initial
forecast for January of $1.80. Use ? = .1, then ? = .3, and finally ? = .5.
Using MAD, which a is the best?
