The product is designed to improve the night sleep, so the company is guessing that sales of the product is somewhat related to sleeping pattern of customers over the days of the week.Before mass production of the product, Eli Orchid has market tested Orchid Relief in only Orange County over the past 8 weeks. The weekly demand is recorded. Eli Orchid is now trying to use the sales pattern over the past 8 weeks to predict sales in US for the upcoming few weeks, especially for days 57 and 60. An accurate forecast would be much helpful in arrangements for the company's production processes and design of price promotions over each week.

# of days Daily Demand

1 297

2 293

3 327

4 315

5 348

6 447

7 431

8 283

9 326

10 317

11 345

12 355

13 428

14 454

15 305

16 310

17 350

18 328

19 366

20 460

21 427

22 291

23 325

24 354

25 322

26 405

27 442

28 450

29 318

30 298

31 355

32 355

33 374

34 447

35 463

36 291

37 319

38 333

39 339

40 416

41 475

42 459

43 319

44 326

45 356

46 340

47 395

48 465

49 453

50 307

51 324

52 350

53 348

54 384

55 474

56 485

Answer the following (show working to get a good rating):

1. How many seasons are in each repeating cycle?

2. To make a forecast of day 60, what is the seasonal factor for this day?

3. Looking at the deseasonalized time series, what is the slope of a linear time trend in the deseasonalized data?