You have been tasked with creating a forecasting model for a product family of sofas at your facility. You are now at the end of Month 36. You searched your demand database and obtained the actual demand for the product family (refer to Table 1 below).

Assignment: Create a linear regression model with seasonality to forecast for Months 37 54.

Submission Requirements:

1. Type and submit a Word report to the dropbox with the following: (a) a title page & a second page answering the following questions:
2. What is your regression equation?
3. What is the Average Seasonal Index for each month (season) of the year? Create a table with your average seasonal indexes (January December).
4. What are your forecasts for the next 18 months (37 54)? Create a table with your forecasts (Trend + Seasonality Forecasts).

Table 1: Demand Data for Sofa Product Family

Period	Month	Demand (units)
1	Jan.	1150
2	Feb.	810
3	Mar.	750
4	Apr.	720
5	May	720
6	June	760
7	Jul.	680
8	Aug.	650
9	Sep.	640
10	Oct.	700
11	Nov.	850
12	Dec.	900
13	Jan.	1100
14	Feb.	820
15	Mar.	780
16	Apr.	740
17	May	730
18	June	750
19	Jul.	660
20	Aug.	660
21	Sep.	660
22	Oct.	780
23	Nov.	920
24	Dec.	1280
25	Jan.	1250
26	Feb.	1080
27	Mar.	980
28	Apr.	900
29	May	900
30	June	900
31	Jul.	900
32	Aug.	780
33	Sep.	780
34	Oct.	880
35	Nov.	1170
36	Dec.	1260