Please help me and answer the questions below using the excel data at the bottom. Thank you

Eli Orchid has designed a new pharmaceutical product, Orchid Relief, which improves the night sleep. Before initiating mass production of the product, Eli Orchid has been market-testing Orchid Relief in Orange County over the past 8 weeks. The daily demand values are recorded in the Excel file provided. Eli Orchid plans on using the sales data to predict sales for the upcoming week. An accurate forecast would be helpful in making arrangements for the company's production processes and designing promotions.

The COO of the company approved the initial analysis and asked for the following extensions[1]:

To fit a new multiple regression model with dummy variables for weekdays (not the weekend), and to provide the regression equation (d = a*t + b₁x₁ + b₂x₂ + b₃x₃ + b₄x₄ + b₅x₅ + b₆x₆+ c), along with Adjusted R².

d =

Adjusted R²=

To use all three models:

• M1: d = 1.0356t + 339.29
• M2:d=0.7163t+116.7679w+315.0262
• M3: (the one considering weekdays)

to predict the demand for seven days ahead (Mon, Tue, ..., Sun) and find the total weekly demand.

	M1	M2	M3
Mon.
Tue.
Wed.
Thu.
Fri.
Sat.
Sun.
TOTAL:

Take advantage of the fact that new demand data became available and use this new data to compare the forecasts using MAPE for days 57-63.

New: M: 311 T: 341 W: 357 Th: 363 F: 390 Sa: 490 Su: 492

MAPE_M1:

MAPE_M2:

MAPE_M3:

To provide a line chart with the actual demand (including the new data) and M2 and M3.

To choose the best model for forecasting daily demand at Orchid Relief for 7 days ahead and a short paragraph explaining your choice.

[paragraph here]

[1] Round numbers to four decimal points (e.g. 0.1234), unless explicitly requested otherwise.

Day	Date	Weekday	Daily Demand	Weekend
1	4/25/2016	Mon	297	0
2	4/26/2016	Tue	293	0
3	4/27/2016	Wed	327	0
4	4/28/2016	Thu	315	0
5	4/29/2016	Fri	348	0
6	4/30/2016	Sat	447	1
7	5/1/2016	Sun	431	1
8	5/2/2016	Mon	283	0
9	5/3/2016	Tue	326	0
10	5/4/2016	Wed	317	0
11	5/5/2016	Thu	345	0
12	5/6/2016	Fri	355	0
13	5/7/2016	Sat	428	1
14	5/8/2016	Sun	454	1
15	5/9/2016	Mon	305	0
16	5/10/2016	Tue	310	0
17	5/11/2016	Wed	350	0
18	5/12/2016	Thu	308	0
19	5/13/2016	Fri	366	0
20	5/14/2016	Sat	460	1
21	5/15/2016	Sun	427	1
22	5/16/2016	Mon	291	0
23	5/17/2016	Tue	325	0
24	5/18/2016	Wed	354	0
25	5/19/2016	Thu	322	0
26	5/20/2016	Fri	405	0
27	5/21/2016	Sat	442	1
28	5/22/2016	Sun	454	1
29	5/23/2016	Mon	318	0
30	5/24/2016	Tue	298	0
31	5/25/2016	Wed	355	0
32	5/26/2016	Thu	355	0
33	5/27/2016	Fri	374	0
34	5/28/2016	Sat	447	1
35	5/29/2016	Sun	463	1
36	5/30/2016	Mon	291	0
37	5/31/2016	Tue	319	0
38	6/1/2016	Wed	333	0
39	6/2/2016	Thu	339	0
40	6/3/2016	Fri	416	0
41	6/4/2016	Sat	475	1
42	6/5/2016	Sun	459	1
43	6/6/2016	Mon	319	0
44	6/7/2016	Tue	326	0
45	6/8/2016	Wed	356	0
46	6/9/2016	Thu	340	0
47	6/10/2016	Fri	395	0
48	6/11/2016	Sat	465	1
49	6/12/2016	Sun	453	1
50	6/13/2016	Mon	307	0
51	6/14/2016	Tue	324	0
52	6/15/2016	Wed	350	0
53	6/16/2016	Thu	348	0
54	6/17/2016	Fri	384	0
55	6/18/2016	Sat	474	1
56	6/19/2016	Sun	485	1