# Dat summarycentfit1 call lmformula y x ix2

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Unformatted text preview: 304 640 7 308 590 8 315 560 > xbar = mean(cent.dat[,1]) > xbar [1] 297 > cent.dat\$xcent = cent.dat\$x - xbar > cent.dat x y xcent 1 280 770 -17 2 284 800 -13 3 292 840 -5 4 295 810 -2 5 298 735 1 6 304 640 7 7 308 590 11 8 315 560 18 > cent.fit1 = lm(y ~ x + I(x^2), cent.dat) > summary(cent.fit1) Call: lm(formula = y ~ x + I(x^2), data = cent.dat) Residuals: 1 2 -24.09 -1.93 3 52.91 4 5 6 7 38.97 -14.25 -48.53 -45.33 8 42.24 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.503e+04 1.241e+04 -2.016 0.0998 . x 1.812e+02 8.364e+01 2.167 0.0825 . I(x^2) -3.179e-01 1.407e-01 -2.259 0.0734 . --Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 47.54 on 5 degrees of freedom Multiple R-Squared: 0.8596, Adjusted R-squared: 0.8035 F-statistic: 15.31 on 2 and 5 DF, p-value: 0.007383 > cent.fit2 = lm(y ~ xcent + I(xcent^2), cent.dat) > summary(cent.fit2) Call: lm(formula = y ~ xcent + I(xcent^2), data = cent.dat) Residuals: 1 2 -24.09 -1.93 3 52.91 4 5 6 7 38.97 -14.25 -48.53 -45.33 8 42.24 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 757.1458 24.1013 31.415 6.14e-07 *** xcent -7.5775 1.5175 -4.993 0.00413 ** I(xcent^2) -0.3179 0.1407 -2.259 0.07344 . --Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 47.54 on 5 degrees of freedom Multiple R-Squared: 0.8596, Adjusted R-squared: 0.8035 F-statistic: 15.31 on 2 and 5 DF, p-value: 0.007383 > cent.dat\$x2 = > cent.dat x y xcent 1 280 770 -17 2 284 800 -13 3 292 840 -5 4 295 810 -2 5 298 735 1 6 304 640 7 7 308 590 11 8 315 560 18 cent.dat\$x^2 x2 78400 80656 85264 87025 88804 92416 94864 99225 > cor(cent.dat\$x,cent.dat\$x2) [1] 0.9998355...
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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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