1. Using Minitab or equivalent software, fit an ARIMA(0, 0, 0)(0,1,1)12 model to the data in Table 3-12. Why does this model seem to be a reasonable initial choice for Jame? Looking at the plots and autocorrelations of the original series and differences other than Diffl2Sales, is there another candidate model that you might consider?
2. Is the model suggested in Question 1 adequate? Discuss with reference to residual plots, residual autocorrelations, and the Ljung-Box chi-square statistics. If the model is not adequate, modify and refit the initial model until you feel you have achieved a satisfactory model.
3. Using the model you have developed in Question 2, generate forecasts of cookie sales for the next 12 months. Include the forecasts on a plot of the original Surtido Cookies sales series. Write a brief report describing the nature of the forecasts and whether they seem reasonable.
Jame Luna's efforts to model and forecast Surtido Cookies monthly sales have been documented in Cases 3-5, 4-8, 5-7, and 8-8. A time series plot of sales and the corresponding autocorrelations were considered in Case 4-8. In Case 5-7, a decomposition analysis of sales confirmed the existence of a trend component and a seasonal component. A multiple regression model with a time trend and seasonal dummy variables was considered in Case 8-8. At a recent forecasting seminar, Jame encountered ARIMA modeling.