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

a) After plotting the data, we can observe that the most appropriate forecasting technique is [ A.) Moving Average B.) Linear Regression without Cyclic Decomposition C.) Linear Regression with Cyclic Decomposition D.) Exponential Smoothing ].

b) The overall average of demand across all provided periods is [ A.) 138.24 B.) 133.33 C.) 125.50 D.) 129.42 ] units.

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

Period	Seasonal Factor
Jan-Feb	[ A.) 0.80 B.) 0.95 C.) 1.36 D.) 0.87 E.) 0.83 F.) 1.19 ]
Mar-Apr	[ A.) 1.19 B.) 0.87 C.) 0.80 D.) 1.36 E.) 0.95 F.) 0.83 ]
May-Jun	[ A.) 1.36 B.) 0.87 C.) 0.80 D.) 0.95 E.) 0.83 F.) 1.19 ]
Jul-Aug	[ A.) 0.83 B.) 0.95 C.) 0.87 D.) 1.19 E.) 1.36 F.) 0.80 ]
Sep-Oct	[ A.) 0.83 B.) 0.80 C.) 0.87 D.) 0.95 E.) 1.19 F.) 1.36 ]
Nov-Dec	[ A.) 0.87 B.) 0.83 C.) 1.19 D.) 0.80 E.) 1.36 F.) 0.95 ]

d) The regression line fitting the deseasonalized demand has an intercept of [ A.) 123 B.) 1.14 C.) 0.98 D.) 127 ] and a slope of [ A.) 1.13 B.) 127 C.) 0.98 D.) 123 ].

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	[ A.) 164 B.) 112 C.) 118 D.) 133 E.) 189 F.) 114 ]
Mar-Apr	[ A.) 112 B.) 164 C.) 114 D.) 133 E.) 189 F.) 118 ]
May-Jun	[ A.) 112 B.) 118 C.) 114 D.) 133 E.) 164 F.) 189 ]
Jul-Aug	[ A.) 133 B.) 164 C.) 114 D.) 189 E.) 112 F.) 118 ]
Sep-Oct	[ A.) 189 B.) 118 C.) 114 D.) 112 E.) 164 F.) 133 ]
Nov-Dec	[ A.) 189 B.) 114 C.) 133 D.) 112 E.) 164 F.) 118 ]

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