Month	Sales
1	1331
2	1293
3	1276
4	979
5	1012
6	770
7	798
8	836
9	605
10	715
11	836
12	1133
13	1447
14	1309
15	1359
16	1062
17	1062
18	820
19	864
20	886
21	671
22	715
23	919
24	1265
25	1551
26	1403
27	1458
28	1128
29	1155
30	880
31	913
32	957
33	693
34	814
35	952
36	1293
37	1651
38	1513
39	1558
40	1228
41	1255
42	980
43	1013
44	1015
45	798
46	919
47	1052
48	1394

The CSUSM Restaurant just finished its fourth year of operation. Through the great efforts of its manager and staff, this restaurant has become one of the most popular and fastest-growing restaurants in the SD County. The manager has recently decided to improve the capacity planning process of the restaurant. To do so, they need to come up with an effective forecasting procedure to predict the monthly sales of foods for up to a year in advance (12 months). Data file RestaurantCSUSM shows the value of food sales ($100s) for the first four years of operation. You, as the new forecasting analyst, have been asked to propose a new forecasting procedure with a summary of your method, forecasts, and recommendations. Include the followings: 1. A time series plot. Comment on the underlying pattern in the time series. What forecasting technique(s) do you recommend based on your visualization? 2. Do you consider moving average as an effective method for forecasting the food sales of this restaurant? Why? 3. Using dummy variables for seasonality effects, forecast sales for January through December of the fifth year. 4. Add a trend effect to your forecasting model in (3) and forecast sales for January through December of the fifth year again with the new model. 5. Which model is expected to give more accurate forecasts, (3) or (4)? Calculate MSE for both techniques. 6. Assume that restaurant's sales in January of the fifth year turn out to be $165,000. What was your forecast (and what is the forecasting error)? How do you explain this error to the manager?