Question

Answer the following questions and upload to Canvas. Submit in Word or PDF format.  Show your work and upload the Excel sheet as well. All the writing parts must be your original writing, don't quote, write in your own words.

The following table presents the orders of Samson Company for the last 36 months (3 years).

Month	Order Year 1	Order Year 2	Order Year 3
January	502	614	712
February	408	592	698
March	491	584	686
April	456	532	644
May	481	599	694
June	511	604	702
July	522	624	724
August	500	612	716
September	510	625	732
October	512	627	740
November	520	650	745
December	536	680	756

1. Use the data in the above table and regression analysis to forecast the orders for the next 12 months (4th year). Include your excel work sheet and your work in your write up. Show the regression equation, values of intercept, slope, correlation coefficient and coefficient determination and the forecast of orders for the next 12 months.
2. Explain how you could make your forecast’s results more reliable by incorporating a qualitative research to your quantitative results.

Answer 1

Regression output

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.96639242
R Square	0.9339143
Adjusted R Square	0.93197061
Standard Error	24.9413658
Observations	36
ANOVA
	df	SS	MS	F	Significance F
Regression	1	298895.2001	298895.2	480.4835	1.22696E-21
Residual	34	21150.43882	622.0717
Total	35	320045.6389
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	444.425397	8.490072288	52.34648	4.34E-34	427.1714941	461.6793
Period ( t)	8.77129987	0.400151751	21.91993	1.227E-21	7.958093678	9.58450606

Regression equation:

Order ( y ) = 444.425 + 8.771 ( Period )

slope ; 8.771

intercept ; 444.425

Coefficient of determination (R²) = 0.9339

Correlation coefficient (R) ) = 0.9664

forecast the orders for the next 12 months (4th year) :

Period	Forecast ( y = 444.425 + 8.771 ( Period )) Rounded off
37	769
38	778
39	787
40	795
41	804
42	813
43	822
44	830
45	839
46	848
47	857
48	865

2)

For qualitative research, we have to see whether there is any chance disruption in the industry or economy for the next year etc.

if there is a election and calamity etc, the price of the products and hence the demand may be affected.

In those cases, we must factor the above quantitative forecast figures with expert opinion. Otherwise, if the industry, economy and other external factors remain stable, we can use the above figures.