1. Using Minitab or equivalent software, fit an ARIMA(0,1, 0)(0, 0,1)12 model to the data in Table 9-24. Why does this model seem to be a reasonable initial choice? 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 contacts for the next 12 months. Include the forecasts on a plot of the original contacts series. Write a brief report commenting on the reasonableness of the forecasts.
4. Do you have any reservations about using ARIMA modeling in this situation? Discuss.
In Case 4-6, Pat Niebuhr and his team were interested in developing alternative forecasts for total orders and contacts per order (CPO) as part of a staffing plan project for the contact centers of his employer, a large web retailer. Monthly forecasts of total orders and CPO were supplied by the finance department for Pat's use, but since he had some historical data available, he thought he would try to generate alternative forecasts using smoothing methods.

TABLE 9-24 Contacts for Web Retailer, June 2001-June 2003 Month Contacts un-01 Jul-01 Aug-01 Sep-01 Oct-01 Nov-01 Dec-01 Jan-02 Feb-02 Mar-02 561,516 566,056 478,739 398,672 482.174 681,482 906251 649,727 454,580 430,809 387,877 384.972 409,512 394.388 465,686 489.443 534.183 625.216 940,097 627.356 459.306 434.151 397,658 383. 449 437.019 Apr-02 May-02 Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03 un-03