The quarterly earnings (in $ millions) of a large cellphone manufacturer have been recorded for the years 2018-2019. These data are listed here.

For part (a) round all final answers to 2 decimal places.

(a) Fill in the table below given the regression equation, y=165.1+2.9y^=165.1+2.9 tt.

Year	Quarter	tt	yy (actual value)	yy^	Ratio
2018	1		150
	2		200
	3		163
	4		184
2019	1		160
	2		210
	3		170
	4		190

For part (b) round all final answers to 2 decimal places.

(b) Compute the seasonal indexes in the table below.

Quarters	Seasonal Index
1
2
3
4

For part (c) round your numerical answer to the nearest whole number and express percentages in percent form (i.e. 3% instead of 0.03).

(c) The results of the seasonal index indicates that the earnings (in $ millions) for this cellphone company in quarter-1 are %% Select an answer more less than the seasonal average earnings.

For part (d) round your regression values to 1 decimal place and your forecasting times to the nearest whole number.

(d) Forecast the quarterly earnings for the year 2020 by filling in the table below. Seasonal Index forecasting: Ft=[b0+b1t]SItFt=[b0+b1t]SIt.

Next Year	Time Period tt	y=y^= regression at time tt	Seasonal index	Forecasting at time tt
2020

(e) Another forecating model (model-2) was used and the results are shown below. Compute MAD and SSE for each model.

Quarter	1	2	3	4
Forecast (Model 1 from Part (d))
Forecast (Model 2)	168	228	185	200
Actual	170	225	185	204

Round your MAD/SSE values to 2 decimal places.

Model-1 MAD = Model-1 SSE = Model-2 MAD = Model-2 SSE = (f) Based on the above results, the best forecasting model is model

• Model-1
• Model-2