1. Using Mary Beasley's data in Table 4-13, construct a time series plot, and compute the sample autocorrelations and sample partial autocorrelations for the total billable visits, Yt the differenced series, ˆ†12Yt; and the seasonally differenced series, ˆ†12Yt. Given this information and the information in Figures 9-55 and 9-56, do you think Mary's initial model is reasonable? Discuss.
Figure 9-55

Figure 9-56
Sample Autocorrelations for DiffDiff1 2TotVisits

2. Using Minitab or equivalent software, fit an ARIMA(0,1,1)(0,1,1)12 model to Mary Beasley's data, and generate forecasts for the next 12 months. Examine the residuals to check for adequacy of fit. Should Mary be happy with the forecasts of total billable visits now?
3. Looking at a time series plot of total billable visits to Medical Oncology, can you see any feature(s) of these data that might make modeling difficult?
Mary Beasley has been disappointed in her forecasting efforts so far. She has tried Winters' smoothing (see Case 4-7), decomposition (see Case 5-8), and regression (see Case 8-9) with less than satisfactory results. Mary knows her time series of monthly total billable visits to Medical Oncology is nonstationary and seasonal. She knows from one of her Executive M.B.A. courses that ARIMA models can describe time series that are nonstationary and seasonal and decides to give ARIMA modeling a try. She has nothing to lose at this point.

Transcribed Image Text:

Time Series Plot of DiffDiff12TotVisits 300 200 100 -100 200 300 -403 11 22 33 44 55 6778 99 1 Month Autocorrelation Function for DiffDiff12TotVisits 1.0 0.8 0.6 s 04 0.0 02 -0.4 0.8 1.0 2 10 1214 15 18 20 22 24 Lag