1. Develop a naive model to forecast the number of new clients seen by CCC for the rest of 1993.
2. Develop a moving average model to forecast the number of new clients seen by CCC for the rest of 1993.
3. Develop an exponential smoothing procedure to forecast the number of new clients seen by CCC for the rest of 1993.
4. Evaluate these forecasting methods using the forecast error summary measures presented in Chapter 3.
5. Choose the best model and forecast new clients for the rest of 1993.
6. Determine the adequacy of the forecasting model you have chosen.
The Consumer Credit Counseling (CCC) operation was described in Case 1-2. The executive director, Marv Harnishfeger, concluded that the most important variable that CCC needed to forecast was the number of new clients that would be seen in the rest of 1993. Marv provided Dorothy Mercer monthly data for the number of new clients seen by CCC for the period from January 1985 through March 1993 (see Case 3-3). Dorothy then used autocorrelation analysis to explore the data pattern. Use the results of this investigation to complete the following tasks.