9.6 Your marketing department estimates that Medicare urology visits equal 5 (1.0 C ) + (6.5 T O ) + (5 T R ) + (0.01 Y ). Here, C denotes the Medicare copayment (now $20), T O is waiting time in your clinic (now 30 minutes), T R is waiting time in your competitors clinic (now 40 minutes), and Y is per capita income (now $40,000). a. How many visits do you anticipate? b. Medicares allowed fee is $120. What revenue do you anticipate? c. What might change your forecast of visits and revenue?

9.7 Because of fluctuations in insurance coverage, the average price paid out of pocket ( P ) by patients of an urgent care center varied, as the table shows. The number of visits per month ( Q ) also varied, and an analyst believes the two are related. The analyst also thinks the data show a trend. Run a regression of Q on P and Period to test these hypotheses. Then use the estimated parameters a , b , and c and the values of Month and P to predict Q (number of visits). The prediction equation is Q = a + ( b Month ) + ( c P ).

month	1	2	3	4	5	6	7	8	9	10	11	12
P	21	18	15	24	18	21	18	15	20	19	24	20
Q	193	197	256	179	231	214	247	273	223	225	198	211

Use the data in Exercise 9.7 to answer these questions: a. Calculate the nave estimator, which is Q t = Q t 1 . b. Calculate the two-period moving-average forecast. c. Calculate the mean absolute deviation for the regression forecast, the nave forecast, and the two-period moving-average forecast. d. Which forecast seems to perform the best? Why?