Based on past demand (Exhibit 1), use the classical decomposition method to forecast average demand for the upcoming year, by month.

 

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EXHIBIT 1 New residential houses sold. 2014 2015 2016 2017 2018 840 920 1,280 1,320 880 1,200 1,440 1,400 Mar 1,120 1,360 1,640 1,560 Apr 1,200 1,360 1,720 May 1.120 1,400 1,400 June 1,120 1,360 July 1,080 Aug 1,000 Sept Oct Nov Dec Total 12,200 14,760 17,160 17,560 Jan Feb 1,320 1,240 1,240 960 1,200 1,000 1,160 920 1,120 960 1,560 1,800 1,280 1,120 1,240 1,840 1,920 1,880 1,560 1,600 1,720 1,720 1,520 1,760 1,320 1,400 1,720 1,240 1.440 1.640 1,240 1,480 1,440 1,520 1,400 1,560 1,240 1.440 1,400 1.520 20,040 EXHIBIT 1 New residential houses sold. 2014 2015 2016 2017 2018 840 920 1,280 1,320 880 1,200 1,440 1,400 Mar 1,120 1,360 1,640 1,560 Apr 1,200 1,360 1,720 May 1.120 1,400 1,400 June 1,120 1,360 July 1,080 Aug 1,000 Sept Oct Nov Dec Total 12,200 14,760 17,160 17,560 Jan Feb 1,320 1,240 1,240 960 1,200 1,000 1,160 920 1,120 960 1,560 1,800 1,280 1,120 1,240 1,840 1,920 1,880 1,560 1,600 1,720 1,720 1,520 1,760 1,320 1,400 1,720 1,240 1.440 1.640 1,240 1,480 1,440 1,520 1,400 1,560 1,240 1.440 1,400 1.520 20,040

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The classical decomposition method involves breaking down a time series data into its underlying components trend seasonal and residual This method he... View the full answer