The owner of a convenience store near Salt Lake City in Utah has been tabulating weekly sales at the store, excluding gas. The accompanying data file contains the sales for 30 weeks. Click here for the Excel Data File a. Use the 3-period moving average to forecast sales for the 31st week. Note: Do not round intermediate calculations. Round final answer to 2 decimal places. h = b. Use simple exponential smoothing with a 0.3 to forecast sales for the 31st week. Note: Do not round intermediate calculations. Round final answer to 2 decimal places. |ŷt= c-1. Report the performance measures for the techniques in parts a and b. Note: Do not round intermediate calculations. Round final answers to 2 decimal places. Technique Moving average Exponential smoothing MSE MAD MAPE (%) c-2. Which is the preferred technique for making the forecast? because it has the out of 3 performance measures Week Sales 1 4848.3000 2 4969.8000 3 4776.3000 4 4952.7000 4851.9000 567 4950.0000 4754.7000 8 4660.2000 9 4349.7000 10 4511.7000 11 4536.0000 12 4878.0000 13 4602.6000 14 4568.4000 15 4436.1000 16 4707.9000 17 4498.2000 18 4428.0000 19 4476.6000 20 4400.1000 21 4555.8000 22 4254.3000 23 4599.9000 24 4846.5000 25 4532.4000 26 4381.2000 27 4533.3000 28 4818.6000 29 4516.2000 30 4685.4000
The owner of a convenience store near Salt Lake City in Utah has been tabulating weekly sales at the store, excluding gas. The accompanying data file contains the sales for 30 weeks. Click here for the Excel Data File a. Use the 3-period moving average to forecast sales for the 31st week. Note: Do not round intermediate calculations. Round final answer to 2 decimal places. h = b. Use simple exponential smoothing with a 0.3 to forecast sales for the 31st week. Note: Do not round intermediate calculations. Round final answer to 2 decimal places. |ŷt= c-1. Report the performance measures for the techniques in parts a and b. Note: Do not round intermediate calculations. Round final answers to 2 decimal places. Technique Moving average Exponential smoothing MSE MAD MAPE (%) c-2. Which is the preferred technique for making the forecast? because it has the out of 3 performance measures Week Sales 1 4848.3000 2 4969.8000 3 4776.3000 4 4952.7000 4851.9000 567 4950.0000 4754.7000 8 4660.2000 9 4349.7000 10 4511.7000 11 4536.0000 12 4878.0000 13 4602.6000 14 4568.4000 15 4436.1000 16 4707.9000 17 4498.2000 18 4428.0000 19 4476.6000 20 4400.1000 21 4555.8000 22 4254.3000 23 4599.9000 24 4846.5000 25 4532.4000 26 4381.2000 27 4533.3000 28 4818.6000 29 4516.2000 30 4685.4000
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
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Transcribed Image Text:The owner of a convenience store near Salt Lake City in Utah has been tabulating weekly sales at the store, excluding gas. The
accompanying data file contains the sales for 30 weeks.
Click here for the Excel Data File
a. Use the 3-period moving average to forecast sales for the 31st week.
Note: Do not round intermediate calculations. Round final answer to 2 decimal places.
h =
b. Use simple exponential smoothing with a 0.3 to forecast sales for the 31st week.
Note: Do not round intermediate calculations. Round final answer to 2 decimal places.
|ŷt=
c-1. Report the performance measures for the techniques in parts a and b.
Note: Do not round intermediate calculations. Round final answers to 2 decimal places.
Technique
Moving average
Exponential smoothing
MSE
MAD
MAPE (%)
c-2. Which is the preferred technique for making the forecast?
because it has the
out of 3 performance measures
Transcribed Image Text:Week
Sales
1
4848.3000
2
4969.8000
3
4776.3000
4
4952.7000
4851.9000
567
4950.0000
4754.7000
8
4660.2000
9
4349.7000
10
4511.7000
11
4536.0000
12
4878.0000
13
4602.6000
14
4568.4000
15
4436.1000
16
4707.9000
17
4498.2000
18
4428.0000
19
4476.6000
20
4400.1000
21
4555.8000
22
4254.3000
23
4599.9000
24
4846.5000
25
4532.4000
26
4381.2000
27
4533.3000
28
4818.6000
29
4516.2000
30
4685.4000
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