### What holiday results in the maximum sales for the department

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##### Reference no: EM13891624

Dry Goods Sales

The data is for weekly sales in the dry goods department at a Wal*Mart store in the Northeast.

I.e. spikes, usually occur at holiday periods. Week 1 is the first week of February 2003. To s week 1 of 2004 is represented as week 53 since week 53 represents the start of the 2004 fisc values are adjusted in order to disguise true sales figures, but trends in the data are retained fo purposes. Note that for 2002­2003 data, Wal*Mart used 53 weeks for their fiscal year data.

 Week Sales in \$ 41 18000 42 16800 43 15200 44 15000 45 13600 46 16000 47 12600 48 14800 49 16800 50 14800 51 15200 52 16000 53 15600 54 15600 55 15000 56 15700 57 15800 58 13800 59 12800 60 14400 61 15800 62 16000 63 12400 64 16200 65 17000 66 18600 67 16000 68 18000 69 19600 70 18600 71 18450 72 18000 73 18200 74 18600 75 16000 76 15200 77 16800 78 15800 79 17600 80 15800 81 15600 82 14200 83 16600 84 16100 85 14100 86 14400 87 14500 88 16900 89 17000 90 16000 91 17800

Dry Goods 2003-2004

1. Can you identify holiday periods or special events that cause the spikes in the data?

2. What holiday results in the maximum sales for this department?

3. a) Generate linear and quadratic models for this data.

b) What is the marginal sales for this department using each model.

c) Which model do you feel best predicts future trends and explain your rational.

4. Based on the model selected, what type of seasonal adjustments, if any, would be required to meet customer needs?

5. Some items were added or subtracted from the 2003-2004 dry goods department data when compared with the data available for the previous year (2002-2003).

a) Use your best model for the 2003-2004 data set to predict sales for the next four weeks. Provide chart and model backup for predictions.

b) Compute the percent rate of increase (y2-y1) / y1 for the next four weeks using results from part a). Provide appropriate backup material.

c) Using 2002-2003 data, find the percentage rate of increase for the next four weeks. Provide chart, model and computation backup. Note and discuss differences in part b) and part c) results.

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