A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week.   To forecast the demand for Sunday for Week 5, do you need to “deseasonalize” the demands for Weeks 1-4 and then “re-seasonalize” them. Why?

Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter13: Regression And Forecasting Models
Section13.7: Exponential Smoothing Models
Problem 29P: The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building...
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A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week. 

  •  To forecast the demand for Sunday for Week 5, do you need to “deseasonalize” the demands for Weeks 1-4 and then “re-seasonalize” them. Why? 
A restaurant has tracked the number of meals served at lunch over the last four weeks. The data
shows little in terms of trends, but does display substantial variation by day of the week. Use the
following information to determine the seasonal (daily) index for this restaurant.
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
LEHNTENE
1
40
54
61
72
89
91
80
Index
0.5627
0.7855
0.8963
1.0618
1.1800
1.3444
1.1692
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
(Time-series forecasting, moderate)
Week
2
35
55
60
77
80
90
82
3
39
51
65
78
81
99
81
4
43
59
64
69
79
95
83
Transcribed Image Text:A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week. Use the following information to determine the seasonal (daily) index for this restaurant. Day Sunday Monday Tuesday Wednesday Thursday Friday Saturday LEHNTENE 1 40 54 61 72 89 91 80 Index 0.5627 0.7855 0.8963 1.0618 1.1800 1.3444 1.1692 Day Sunday Monday Tuesday Wednesday Thursday Friday Saturday (Time-series forecasting, moderate) Week 2 35 55 60 77 80 90 82 3 39 51 65 78 81 99 81 4 43 59 64 69 79 95 83
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