Dry Goods Sales

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 2002. To show continuity, week 1 of 2003 is represented as week 54 since week 53 represents the end of fiscal 2002 and start of the 2003 fiscal year. Dollar values are adjusted in order to disguise true sales figures, but trends in the data are retained for analysis puposes.

Week Sales in $

26 15200

27 15600

28 16400

29 15600

30 14200

31 14400

32 16400

33 15200

34 14400

35 13800

36 15000

37 14100

38 14400

39 14000

40 15600

41 15000

42 14400

43 17800

44 15000

45 15200

46 15800

47 18600

48 15400

49 15500

50 16800

51 18700

52 21400

53 20900

54 18800

55 22400

56 19400

57 20000

58 18100

59 18000

60 19600

61 19000

62 19200

63 18000

64 17600

65 17200

66 19800

67 19600

68 19600

69 20000

70 20800

71 22800

72 23000

73 20800

74 25000

75 30600

76 24000

77 21200

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

data?

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

a1) 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.

d) Based on the model selected, what type of seasonal adjustments, if any, would be

required to meet customer needs?