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Unformatted text preview: # second way: y_hat = predict(lm.1) plot(y_hat, lm.1$residuals) # The residualplot is a random scatter of dots about the y=0 line, # and so, suggets that the fit is good. # d) lm.2 = lm(y ~ x1 + x2 ) lm.2 # (Intercept) x1 x2 # 38.3508 0.2588 0.3015 # e) summary(lm.2) Sheet1 Page 2 # Multiple Rsquared: 0.3648. # About 36% of the variance in Strength can be attributed to (or explained by) # Depth and and Water Content through the linear expression in lm() . # f) # Given that the R2 increases from 36% to 41%, it's reasonable to conclude # that at least one of the higherorder terms provides useful information # about Strength....
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This document was uploaded on 05/15/2010.
 Spring '08

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