DO NOT COPY AND PASTE OTHER ANSWERS THEY ARE WRONG THANK YOU Sales data for two years are as attached Data are aggregated with two months of sales in each period Months Sales January February 109 March April 104 May June 150 July August 170 September October 120 November December 100 January February 115 March April 112 May June 159 July August 182 September October 126 November December 106 a) After plotting the data, we can observe that the most appropriate forecasting technique is Exponential Smoothing b) The overall average of demand across all provided periods is 129 42 units c) For each period, select the multiplicative seasonal index factor from the dropdown list Period Seasonal Factor Jan Feb Select 1 36 , 1 19 , 0 87 , 0 80 , 0 83 , 0 95 Mar Apr Select 0 87 , 0 80 , 1 19 , 0 83 , 0 95 , 1 36 May Jun Select 0 95 , 0 87 , 0 83 , 1 36 , 1 19 , 0 80 Jul Aug Select 0 95 , 0 83 , 1 36 , 0 87 , 1 19 , 0 80 Sep Oct Select 0 95 , 0 87 , 1 36 , 0 80 , 1 19 , 0 83 Nov Dec Select 0 87 , 0 95 , 1 36 , 0 80 , 1 19 , 0 83 d) The regression line fitting the deseasonalized demand has an intercept of Select 127 , 0 98 , 123 , 1 14 and a slope of Select 0 98 , 123 , 1 13 , 127 e) Using the results from the previous parts, what is the forecast including trend and seasonality (FITS) for each period of the next year Period FITS Jan Feb Select 133 , 112 , 114 , 164 , 118 , 189 Mar Apr Select 189 , 164 , 118 , 114 , 112 , 133 May Jun Select 133 , 112 , 118 , 189 , 164 , 114 Jul Aug Select 133 , 164 , 114 , 112 , 118 , 189 Sep Oct Select 189 , 112 , 114 , 133 , 118 , 164 Nov Dec Select 189 , 164 , 114 , 118 , 133 , 112

Question: DO NOT COPY AND PASTE OTHER ANSWERS THEY ARE WRONG. THANK YOU Sales data for two years are as attached. Data are aggregated with two

DO NOT COPY AND PASTE OTHER ANSWERS THEY ARE WRONG. THANK YOU

Sales data for two years are as attached. Data are aggregated with two months of sales in each "period".

Months Sales January-February 109 March-April 104 May-June 150 July-August 170 September-October 120 November-December 100 January-February 115 March-April 112 May-June 159 July-August 182 September-October 126 November-December 106

a) After plotting the data, we can observe that the most appropriate forecasting technique is Exponential Smoothing .

b) The overall average of demand across all provided periods is 129.42 units.

c) For each period, select the multiplicative seasonal index factor from the dropdown list.

Period	Seasonal Factor
Jan-Feb	[ Select ] ["1.36", "1.19", "0.87", "0.80", "0.83", "0.95"]
Mar-Apr	[ Select ] ["0.87", "0.80", "1.19", "0.83", "0.95", "1.36"]
May-Jun	[ Select ] ["0.95", "0.87", "0.83", "1.36", "1.19", "0.80"]
Jul-Aug	[ Select ] ["0.95", "0.83", "1.36", "0.87", "1.19", "0.80"]
Sep-Oct	[ Select ] ["0.95", "0.87", "1.36", "0.80", "1.19", "0.83"]
Nov-Dec	[ Select ] ["0.87", "0.95", "1.36", "0.80", "1.19", "0.83"]

d) The regression line fitting the deseasonalized demand has an intercept of [ Select ] ["127", "0.98", "123", "1.14"] and a slope of [ Select ] ["0.98", "123", "1.13", "127"] .

e) Using the results from the previous parts, what is the forecast including trend and seasonality (FITS) for each period of the next year?

Period	FITS
Jan-Feb	[ Select ] ["133", "112", "114", "164", "118", "189"]
Mar-Apr	[ Select ] ["189", "164", "118", "114", "112", "133"]
May-Jun	[ Select ] ["133", "112", "118", "189", "164", "114"]
Jul-Aug	[ Select ] ["133", "164", "114", "112", "118", "189"]
Sep-Oct	[ Select ] ["189", "112", "114", "133", "118", "164"]
Nov-Dec	[ Select ] ["189", "164", "114", "118", "133", "112"]

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