1. Discuss the problems, if any, of explaining the Box-Jenkins method to John's banker and others on his management team.
2. Given the autocorrelations in Figure 9-39 and the partial autocorrelations in Figure 9-40, what regular (nonseasonal) terms might John include in an ARIMA model for Mr. Tux sales? What seasonal terms might he include in the model?
Figure 9-39
Autocorrelations for Mr. Tux: Regular and Seasonally Differenced Series

Figure 9-40
Partial Autocorrelations for Mr. Tux: Regular and Seasonally Differenced Series

3. Using a program for ARIMA modeling, fit and check an ARIMA model for Mr. Tux sales. Generate forecasts for the next 12 months.
John Mosby has decided to try the Box-Jenkins method for forecasting his monthly sales data. He understands that this procedure is more complex than the simpler methods he has been trying. He also knows that more accurate forecasts are possible using this advanced method. Also, he has access to the Minitab computer program that can fit ARIMA models. And since he already has the data collected and stored in his computer, he decides to give it a try.

Transcribed Image Text:

Autocorrelation Function: Diffi Diff12Sales Autocurrelation for DarsDITs Lag ACF T L 0.434972 -3.96 16.28 2 0.119297 0.93 17.52 3 0.177448 1.36 20.29 4 -0.013968-0.10 20.31 5 0.223625 1.58 24.84 6 0.126474 0.92 26.30 7 -0.053425 0.38 26.57 8 0.0549570.39 26.85 9 -0.006380 0.05 26.85 10 0.032094 0.23 26.95 11 -0.006053 -0.04 26.96 Autocorrelation Function for Diff1Diff12Sales 04 80 12 0.037127 -0.27 27.09 13 0.024916 0.1827.16 14 0.025145 -0.18 27.22 15 -0.081508-0.58 27.91 16 0.028684 0.20 28.00 17 0.184183 1.31 31.62 18 -0.148161 1.03 34.01 19 0.059354 -0.41 34.39 20 Ο.165452 1.14 37.46 21 -0.044315 -0.30 37-6 -93 24O1 1420 Lag Partial Autocorrelation Function: DITIDIT12Sales Lag PACF 1 -0.434972-3.96 2 -0.380486 -3.47 3 -0.086077-0.78 4 0.013655 0.12 5 -0.232666 -2.12 Partial Autocorrelation Function for Diff1Diff125ales 6 -0.163070 1.49 7 -0.237648-2.17 8-0.061466 -0.56 9 -0.042868 -0.39 10 -0.010757-0.1O 0.4 -0.009411-0.09 12 -0.091044-0.83 13 0.021219 -0.19 14 -0.051663 0.47 15 -0.144650-1.32 16 -0.191300-1.74 17 0.089113 0.81 18 0.032755 0.30 -0.4 19 -0.142079 1.29 20 -0.075125-.68 21 -0.016020 -0.15 2460101214 20