# Excel Time Series Forecasting and Regression Analysis - Statistics HW Help

Time Series Forecasting and Regression Analysis

 SUMMARY OUTPUT Regression Statistics Multiple R 0.945855 R Square 0.894641 Adjusted R Square 0.877081 Standard Error 2.626558 Observations 8 ANOVA df SS MS F Significance F Regression 1 351.4821 351.4821 50.94823 0.000381 Residual 6 41.39286 6.89881 Total 7 392.875 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -5739.71 809.1556 -7.09346 0.000394 -7719.65 -3759.78 Year 2.892857 0.405287 7.137803 0.000381 1.901155 3.884559

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$Riders=-5,739.71+2.892857\text{ }Year$ $Riders=-5,739.71+2.892857\times 2005=60.46429$
 SUMMARY OUTPUT Regression Statistics Multiple R 0.939361 R Square 0.882399 Adjusted R Square 0.865599 Standard Error 9.512398 Observations 9 ANOVA df SS MS F Significance F Regression 1 4752.6 4752.6 52.52321 0.00017 Residual 7 633.4 90.48571 Total 8 5386 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -8756.85 1223.751 -7.15574 0.000184 -11650.6 -5863.14 Year 4.45 0.614023 7.24729 0.00017 2.998068 5.901932
$Deer\text{ }Strikes = -8756.85+4.45\text{ }Year$ $Deer\text{ }Strikes = -8756.85+4.45\times 2005=165.4$ The model is
$Sales=-4003.17+2.011905\text{ }Year$ $Sales=-4003.17+2.011905\times 2006=32.71429$ The model is
$Weather\text{ }Alarms=-979.92+0.510714\,Year$ $Weather\text{ }Alarms = -979.92+0.510714\times 2006 = 44.57262$ $Sales=-4,821.32+2.485\text{ }Year$ $Sales=-4,821.32+2.485\times 2005=161.6912$
 SUMMARY OUTPUT Regression Statistics Multiple R 0.960277 R Square 0.922131 Adjusted R Square 0.906557 Standard Error 0.736788 Observations 7 ANOVA df SS MS F Significance F Regression 1 32.14286 32.14286 59.21053 0.000591 Residual 5 2.714286 0.542857 Total 6 34.85714 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -2120.21 277.5053 -7.64027 0.000611 -2833.56 -1406.87 Year 1.071429 0.13924 7.694838 0.000591 0.713502 1.429356
$Sales =-2,120.21+1,071429\text{ }Year$ $Sales=-2,120.21+1,071429\times 1999=21.57143$
 SUMMARY OUTPUT Regression Statistics Multiple R 0.928931 R Square 0.862914 Adjusted R Square 0.828642 Standard Error 24.15308 Observations 6 ANOVA df SS MS F Significance F Regression 1 14688.51 14688.51 25.17867 0.007397 Residual 4 2333.486 583.3714 Total 5 17022 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 58196.37 11544.5 5.041047 0.007277 26143.64 90249.11 Year -28.9714 5.773691 -5.01783 0.007397 -45.0018 -12.9411
$Scrap\text{ }Rate=58,196.37-28.9714\text{ }Year$ $Scrap\text{ }Rate=58,196.37-28.9714\times 2004=137.6286=$ $Tons\text{ }of\text{ }Grain=-8,928.54+4.5357\text{ }Year$ $Tons\text{ }of\text{ }Grain=-8,928.54+4.5357\times 1985=74.85714$ The model is
$Car\text{ }Theft\text{ }Rate=15,164.5-77.6071\text{ }Year$ $Car\text{ }Theft\text{ }Rate=15,164.5-77.6071\times 2001=872.6071$ $Car\text{ }Theft\text{ }Rate=15,164.5-77.6071\times 2002=795$ $Car\text{ }Theft\text{ }Rate=15,164.5-77.6071\times 2003=717.3929$