Question

6. Make revenue forecast for Pacific Shoes for 2020 based on historical data if the company generated the following revenues for the last five years. Also, calculate the forecast error, draw a graph with the actual and the regression line revenue by year, and show the forecast error on the graph.
Year 2015 2016 2017 2018 2019
Revenue (Million $) 52 58 62 59 66

Answer 1

ANSWER::

On regressing Revenue (Million) $ on Year , assuming Year=2015 as t=0 and so on, the result is:

Year	Revenue (Million $)	t		SUMMARY OUTPUT
2015	52	0
2016	58	1		Regression Statistics
2017	62	2		Multiple R	0.885728472
2018	59	3		R Square	0.784514925
2019	66	4		Adjusted R Square	0.712686567
				Standard Error	2.774887385
				Observations	5
				ANOVA
					df	SS	MS	F	Significance F
				Regression	1	84.1	84.1	10.92207792	0.04556729
				Residual	3	23.1	7.7
				Total	4	107.2
					Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
				Intercept	53.6	2.149418526	24.93697684	0.000141394	46.75959095	60.44040905	46.75959095	60.44040905
				t	2.9	0.877496439	3.304856717	0.04556729	0.107414701	5.692585299	0.107414701	5.692585299

The OLS regression is: Revenue (Million $) = 53.6 + 2.9*t

The Actual versus Forecasted Revenue (Million $) along with Forecast Error is:

Forecast Revenue (Million $) = 53.6 + 2.9*t

Forecast Error = Actual Revenue - Forecast Revenue

Year	Actual Revenue (Million $)	t	Forecasted Revenue (Million $)	Forecast Error (Million $)
2015	52	0	53.6	-1.6
2016	58	1	56.5	1.5
2017	62	2	59.4	2.6
2018	59	3	62.3	-3.3
2019	66	4	65.2	0.8

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