In a multiplicative Decomposition model for a quarterly time series, the seasonal indexes are 1.40 (December), 1.05 (March), 0.65 (June), and 0.80 (September). If the trend forecast (extrapolating the deseasonalised data) for December 2016 is 8.75 then the forecast for December 2016 including the seasonal component will be

Select one:

a.8.75

b.12.25 this is the correct answer (how do I get this)

c.9.1875

d.10.15

The table above is an extract of a multiplicative decomposition model for quarterly Beer consumption (Megalitres - ML) for the period Jun-2012 to Mar-2017 (20 observations). The middle portion of the spreadsheet has been omitted for space reasons.

Suppose a linear trend equation applied to the Deseasonalised data is

Trend = 439. 46 - 0.289 * Time (Time =1 for Jun-12 ).

The forecast for Dec-17 would be closest to

Select one:

a.515.05

b.390.05

c.502.81

d.439.46

e.None of the above

The table above is from a monthly WES multiplicative spreadsheet model for Sales of a company selling stationery. The entire time series is from Nov-2010 to Nov 2014 although only the last 12 months of the spreadsheet (Dec-13 to Nov-14) are shown in the table above.

From the table, the forecast you would make for Dec-14 would be

Select one:

a.3233.30

b.3241.72

c.3256.86

d.3615.12 this is the correct answer (how do I get this)

e.Not able to be determined since the smoothing parameters are not provided

Quarter Beer (mL) 4MA Centred MA Seas Rels Seas Index Deseas 408 416 X Jun12 Sep-12 Dec-12 Mar-13 0.9 0.94 119 452.35 444.63 438.28 419.36 520 409 435.75 431.25 437 433.5 119 0.94 0.98 Jur16 438.5 433.25 438 435.83 0.87 0.97 Sep-16 379 424 521 409 0.9 0.94 119 420.2 453.18 439.12 Y Dec-16 Mar-17 0.98 Seasonal Forecast Sales 3413.6 3772.6 28843 3077.1 Level 3054.62 3108.77 3103.42 3106.01 3142.38 3148.19 3162.05 Trend 7.14 16.54 12.16 10.25 15.47 111 0.99 0.95 1 Dates Dec-13 Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jurr14 Jul-14 Aug-14 Sep-14 Oct-14 3387.6 X 2998.95 3175.15 2979.89 3196.42 30.11.89 3129.29 0.96 3104.8 3147.5 13.54 101 30.12.41 3207.6 0.95 0.99 1.62 3166.6 3148.9 3424.2 13.6 16.78 12.01 14.01 15.58 3191.54 3184.49 3216.49 3028.32 3241.72 0.97 3282.56 3100.47 3383.1 105 3233.3 15.14 1 3244.19 Nov-14 Dec-14