Regression Statistics

Published: 2017-12-02 14:28:53
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SUMMARY OUTPUTForce Constant to Zero

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FALSE

Regression Statistics

Multiple R0.197

R Square0.039Goodness of Fit < 0.80

Adjusted R Square-0.081

Standard Error11.383

Observations10

ANOVA

dfSSMSFP-value

Regression141.7459144841.745914480.3221588770.586

Residual81036.654086129.5817607

Total 9 1078.4 Confidence Level

0.950.99

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99% Upper 99%

Intercept 27.72435254 13.39697478 2.069448737 0.072 -3.169126685 58.61783176 -17.2277 72.67639

last year wins -0.2895001 0.510051075 -0.567590413 0.586 -1.465679988 0.886679787 -2.00092 1.421919

y = 27.724 -0.29*last year wins

RESIDUAL OUTPUT

Observations

Predictedthis year's wins

Residuals

Standard Residuals

Sorted Residuals

Percentilethis year's wins

118.17085-15.17085-1.41356-15.170855.000003

221.06585-0.06585-0.00614-12.2503515.0000011

321.3553511.644651.08500-9.7763525.0000012

420.77635-9.77635-0.91092-4.8813535.0000013

524.25035-12.25035-1.14144-0.3553545.0000021

621.35535-0.35535-0.03311-0.0658555.0000021

717.88135-4.88135-0.454822.8291565.0000021

818.4603514.539651.3547511.6446575.0000033

918.170852.829150.2636113.4866585.0000033

1022.5133513.486651.2566314.5396595.0000036

The equation:

y = 27.724 -0.29*last year wins

y = 27.724 -0.29*last year wins

y = 27.724 -0.29*last year wins

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