1. Using the data through 2001 in Table 5-12, develop a model to forecast the seasonally adjusted sales data and generate forecasts for the first nine months of 2002.
2. Using the forecasts from part 1, forecast sales for the first nine months of 2002 by adding or subtracting the appropriate seasonal index in Table 5-11. Are these forecasts accurate when compared with the actual values?
3. Forecast sales for October 2002 using the same procedure as in part 2.
4. Compare the pattern of the retail sales data presented in Case 3-1A with the pattern of the actual sales data from 1992 through 1995 presented in Case 4-4 with the pattern of the actual sales data from 1996 through 2001 presented in this case.

In Case 4-4, Julie Murphy developed a naive model that combined seasonal and trend estimates (similar to Equation 4.5). One of the major reasons why she chose this naive model was its simplicity. Julie knew that her father, Glen, would need to understand the forecasting model used by the company.

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Summary of the Decomposition Model for Murphy Brothers Furniture TABLE 5-11 Time Series Decomposition Trend-Line Equation T 5,672 31.4t Seasonal Index Period Inder -674.60 -702.56 -143,72 -366.64 -53.52 -173.27 -42.74 222.32 -57.95 145.76 612.30 1234.63 Measures 10 12 Accuracy MAPE MAD: MSD 1.9 135.1 0.965.3 TABLE 5-12 Seasonally Adjusted Monthly Sales for Murphy Brothers Furniture, 1996-2002 1 2 Jan. Feb. Mar. Ap May Jun. Jl Aug. Sep. Oct Nox. Dec. 1996 5,621 5,671 5,745 5,821 5.775 5,863 5,847 5,818 5,901 5,941 5,857 5,767 1997 6,091 6,096 6,051 6,135 6,161 6.189 6,174 6,277 6.307 6 1998 6551 6,521 6486 6,510 6,496 6,580 6588 6536 6,543 6,659 6,749 6.844 1999 6,736 6,890 6,936 6,954 6,972 7093 7,073 7.269 7.363 7,425 7,401 7.492 2000 7451 7,550 7675 7.700 7,739 7.691 7.715 7.770 7,703 7,777 7685 7.302 2001 7.680 7558 7,564 7,550 7,608 7,648 7,730 7,700 7,484 7,590 7,871 8,094 2002 7.795 7827 7.961 7.905 7,975 7.930 7859 7,986 7,886 6.334 6.380