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# WeightedLS - p-value 3.710e-06> anova(m1 Analysis of...

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Weighted Least Squares and Lack of Fit Test This handout reproduces many of the calculations in Chapter 5 of ALR. > library(alr3) > data(physics) > physics x y SD 1 0.345 367 17 2 0.287 311 9 3 0.251 295 9 4 0.225 268 7 5 0.207 253 7 6 0.186 239 6 7 0.161 220 6 8 0.132 213 6 9 0.084 193 5 10 0.060 192 5 > plot(y ~ x, data=physics) > m1 <- lm(y ~ x, data=physics, weights = 1/SD^2) > abline(m1) > summary(m1) Call: lm(formula = y ~ x, data = physics, weights = 1/SD^2) Residuals: Min 1Q Median 3Q Max -2.323e+00 -8.842e-01 1.266e-06 1.390e+00 2.335e+00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 200 250 300 350 x y

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Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 148.473 8.079 18.38 7.91e-08 *** x 530.835 47.550 11.16 3.71e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.657 on 8 degrees of freedom Multiple R-squared: 0.9397, Adjusted R-squared: 0.9321 F-statistic: 124.6 on 1 and 8 DF,
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Unformatted text preview: p-value: 3.710e-06 > anova(m1) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) x 1 341.99 341.99 124.63 3.710e-06 *** Residuals 8 21.95 2.74 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > RSS <- sum(residuals(m1)^2/physics\$SD^2) [1] 21.95265 The lack-of-fit test compares RSS/ g G = RSS, since / g G = 1 by construction, to the ± G distribution with df given by the df in RSS: > pval1 <- pchisq(RSS, df.residual(m1), lower.tail = FALSE) [1] 0.005004345 The tiny p-value indicates lack of fit. As discussed in the book, a quadratic fits better: > plot(y ~ x, physics, type = "p") > abline(m1) > m2 <- update(m1, ~. + I(x^2)) > lines(physics\$x, predict(m2),lty = 2, col = 2) > chistat <- sum(residuals(m2)^2/physics\$SD^2 > pval2 <- pchisq(chistat), df.residual(m1), lower.tail = FALSE) [1] 0.9194173 0.05 0.10 0.15 0.20 0.25 0.30 0.35 200 250 300 350 x y...
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