9766 adjusted r squared 09726 f statistic 2402 on 4

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Unformatted text preview: 1 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9307 on 23 degrees of freedom Multiple R-Squared: 0.9766, Adjusted R-squared: 0.9726 F-statistic: 240.2 on 4 and 23 DF, p-value: < 2.2e-16 > plot(fit3$fitted.values,fit3$residuals) > abline(h=0,lty=2) > predict.lm(fit3,data.frame(Speed=55),interval=c("prediction"),level=0.95) fit lwr upr [1,] 18.31717 16.28547 20.34887 > plot(Speed, MPG) > y3 = fit3$coeff[1] + fit3$coeff[2]*x + fit3$coeff[3]*x^2 + fit3$coeff[4]*x^3 + fit3$coeff[5]*x^4 > lines(x,y3) > par(mfrow=c(2,2)) > plot(fit) > plot(fit2) > plot(fit3) > shapiro.test(fit3$residuals) Shapiro-Wilk normality test data: fit3$residuals W = 0.9822, p-value = 0.9 > fit4 = lm(MPG ~ Speed + I(Speed^2) + I(Speed^3) + I(Speed^4) + I(Speed^5) + I(Speed^6)) > summary(fit4) Call: lm(formula = MPG ~ Speed + I(Speed^2) + I(Speed^3) + I(Speed^4) + I(Speed^5) + I(Speed^6)) Residuals: Min 1Q Median -1.1129 -0.5717 -0.1707 3Q 0.5025 Max 1.5288 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.421e+01 1.204e+01 -1.180 0.2514 Speed 4.203e+00 2.553e+00 1.646 0.1146 I(Speed^2) -3.521e-01 2.012e-01 -1.750 0.0947 . I(Speed^3) 1.579e-02 7.691e-03 2.053 0.0527 . I(Speed^4) -3.473e-04 1.529e-04 -2.271 0.0338 * I(Speed^5) 3.585e-06 1.518e-06 2.362 0.0279 * I(Speed^6) -1.402e-08 5.941e-09 -2.360 0.0280 * --Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8657 on 21 degrees of freedom Multiple R-squared: 0.9815, Adjusted R-squared: 0.9762 F-statistic: 186 on 6 and 21 DF, p-value: < 2.2e-16 > plot(fit4$fitted.values,fit4$residuals) > abline(h=0,lty=2) > plot(Speed, MPG) > y4 = fit4$coeff[1] + fit4$coeff[2]*x + fit4$coeff[3]*x^2 + fit4$coeff[4]*x^3 + fit4$coeff[5]*x^4 + fit4$coeff[6]*x^5 + fit4$coeff[7]*x^6 > lines(x,y4) &...
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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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