Use the regression and seasonal indexes to forecast demand for the next four quarters. Note: Round your intermediate calculations and answers to 2 decimal places. Period I 11 III IV Forecast (Units)

Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
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Q29 please help me!!

Zeus Computer Chips, Incorporated, used to have major contracts to produce the Centrino-type chips. The market has been declining
during the past three years because of the quad-core chips, which it cannot produce, so Zeus has the unpleasant task of forecasting
next year. The task is unpleasant because the firm has not been able to find replacement chips for its product lines. Here is demand
over the past 12 quarters:
QUARTER TWO
YEARS AGO
I
II
III
IV
DEMAND
4,785
3,485
4,285
2,985
Period
I
||
III
IV
QUARTER
LAST
YEAR
I
II
III
IV
DEMAND
3,485
- 2,685
3,485
2,385
Forecast (Units)
QUARTER
THIS
YEAR
I
II
III
IV
Use the regression and seasonal indexes to forecast demand for the next four quarters.
Note: Round your intermediate calculations and answers to 2 decimal places.
DEMAND
3,185
2,185
2,685
1,685
Transcribed Image Text:Zeus Computer Chips, Incorporated, used to have major contracts to produce the Centrino-type chips. The market has been declining during the past three years because of the quad-core chips, which it cannot produce, so Zeus has the unpleasant task of forecasting next year. The task is unpleasant because the firm has not been able to find replacement chips for its product lines. Here is demand over the past 12 quarters: QUARTER TWO YEARS AGO I II III IV DEMAND 4,785 3,485 4,285 2,985 Period I || III IV QUARTER LAST YEAR I II III IV DEMAND 3,485 - 2,685 3,485 2,385 Forecast (Units) QUARTER THIS YEAR I II III IV Use the regression and seasonal indexes to forecast demand for the next four quarters. Note: Round your intermediate calculations and answers to 2 decimal places. DEMAND 3,185 2,185 2,685 1,685
Expert Solution
Step 1

The approach known as seasonal linear regression (SLR) uses a linear function to obtain the seasonal prediction. The trend and seasonality patterns that it discovers in the historical data can be taken into account. SLR is frequently expected to produce findings that are more reliable than triple exponential smoothing techniques. As we have explained & worked in excel for the time series data given in the problem, seasonal linear regression is a very potent technique that can also produce results that are more potent than triple exponential smoothing for particular time-series data. Both seasonality and trend are fully captured by the algorithm for the future projection.

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