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

Question

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 2
1
y y /y
− 1 for the next four weeks using
results from part a). Provide appropriate backup material.

Answer 1

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

Answer 2

The data is for weekly sales in the dry goods department at a Wal*Mart store in the Northeast. Peak values, I.e. spikes, usually occur at holiday periods. Week 1 is the first week of February 2003. To show continuity, week 1 of 2004 is represented as week 53 since week 53 represents the start of the 2004 fiscal year. Dollar values are adjusted in order to disguise true sales figures, but trends in the data are retained for analysis purposes. Note that for 2002-2003 data, Wal*Mart used 53 weeks for their fiscal year data.