in attempting to forecast the future demand for its products using a time-series forecasting model where sales/ demand is dependent on the time-period (month), a manufacturing firm builds a simple linear regression model. the linear regression output is given below:
SUMMARY OUTPUT Regression Stas Multiple 0.942444261 R Square 0.64945812 Adjusted R Square 0.964261321 Standard Co 2.685037593 Obsero 24 ANOVA Regression Residus Total $ MS F Significancer 1 10377.01761 1037701701 149.567816 1,524436 21 22158.6073913 7 200428877 23 10515.25 Intercept X Variables Comce Standardmor Lower 09 Uper SS LOWESSOS 38076086 11315418943365568547 2,037402035707474042230444 35.72982747 00.42264 3.003013043 0070177439 37.93400239 1.5403212839708085 3.188117002 2039700011117002
What is the estimated simple linear regression equation? 1) Forecast demand (Y) - 3.004 + 38.076 X 2) Forecast demand (Y) - 38.076 +3.004 X 3) Forecast demand (Y) - 0.985 +3.004 X 4) Forecast demand (Y) - 3.004 +0.985 X
