# Df rss df sum of sq f prf 1 23 199215 2 21 157387 2

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Unformatted text preview: gt; anova(fit3,fit4) Analysis of Variance Table Model 1: MPG ~ Speed + I(Speed^2) + I(Speed^3) + I(Speed^4) Model 2: MPG ~ Speed + I(Speed^2) + I(Speed^3) + I(Speed^4) + I(Speed^5) + I(Speed^6) Res.Df RSS Df Sum of Sq F Pr(>F) 1 23 19.9215 2 21 15.7387 2 4.1828 2.7905 0.0842 . --Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(fit,fit3) Analysis of Variance Table Model 1: MPG ~ Speed + I(Speed^2) Model 2: MPG ~ Speed + I(Speed^2) + I(Speed^3) + I(Speed^4) Res.Df RSS Df Sum of Sq F Pr(>F) 1 25 69.174 2 23 19.922 2 49.252 28.432 6.066e-07 *** --Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > fit5 = lm(MPG ~ Speed + I(Speed^2) + I(Speed^3) + I(Speed^4) + I(Speed^5) + I(Speed^6) + I(Speed^7) + I(Speed^8)) > summary(fit5) Call: lm(formula = MPG ~ Speed + I(Speed^2) + I(Speed^3) + I(Speed^4) + I(Speed^5) + I(Speed^6) + I(Speed^7) + I(Speed^8)) Residuals: Min 1Q Median -1.21938 -0.50464 -0.09105 3Q 0.49029 Max 1.45440 Coefficients: (Intercept) Speed I(Speed^2) I(Speed^3) I(Speed^4) I(Speed^5) I(Speed^6) I(Speed^7) I(Speed^8) Estimate Std. Error t value Pr(>|t|) -2.202e+01 7.045e+01 -0.313 0.758 6.021e+00 2.014e+01 0.299 0.768 -5.037e-01 2.313e+00 -0.218 0.830 2.121e-02 1.408e-01 0.151 0.882 -4.008e-04 5.017e-03 -0.080 0.937 1.789e-06 1.080e-04 0.017 0.987 4.486e-08 1.381e-06 0.032 0.974 -6.456e-10 9.649e-09 -0.067 0.947 2.530e-12 2.835e-11 0.089 0.930 Residual standard error: 0.9034 on 19 degrees of freedom Multiple R-squared: 0.9818, Adjusted R-squared: 0.9741 F-statistic: 128.1 on 8 and 19 DF, p-value: 7.074e-15 > cent.dat x y 1 280 770 2 284 800 3 292 840 4 295 810 5 298 735 6...
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## This note was uploaded on 04/03/2014 for the course STAT 420 taught by Professor Stepanov during the Spring '08 term at University of Illinois, Urbana Champaign.

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