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stepwise regression, splus, results interpretation

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jf

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Feb 7, 2007, 5:19:22 PM2/7/07
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I used forward and backward stepwise regression and here is the last
entry
before it spits out a linear model:

Step: AIC= 9.594

ExpDec ~ Ck + Yr + JD + JD2 + QNxtDy + QAve3 + WTDy + WTAve3 + DDCum +
L +
Yr:JD + QNxtDy:QAve3 + QAve3:JD + WTAve3:JD + L:JD

Single term deletions

Model:

ExpDec ~ Ck + Yr + JD + JD2 + QNxtDy + QAve3 + WTDy +

WTAve3 + DDCum + L + Yr:JD + QNxtDy:QAve3 +

QAve3:JD + WTAve3:JD + L:JD

scale: 0.009112796

Df Sum of Sq RSS Cp

<none> 9.30242 9.59403

Ck 1 1.239157 10.54158 10.81496

JD2 1 0.389247 9.69167 9.96505

WTDy 1 0.085947 9.38837 9.66175

DDCum 1 2.607181 11.90960 12.18299

Yr:JD 1 0.112556 9.41498 9.68836

QNxtDy:QAve3 1 1.081182 10.38360 10.65699

QAve3:JD 1 0.038569 9.34099 9.61438

WTAve3:JD 1 0.517758 9.82018 10.09357

L:JD 1 0.116229 9.41865 9.69204

*** Linear Model ***

Call: lm(formula = ExpDec ~ Ck + Yr + JD + JD2 + QNxtDy +

QAve3 + WTDy + WTAve3 + DDCum + L + Yr:JD +

QNxtDy:QAve3 + QAve3:JD + WTAve3:JD + L:JD,

data = global.02.03.07, na.action = na.exclude)

Residuals:

Min 1Q Median 3Q Max

-0.7327 -0.04916 0.001962 0.05493 0.2442

Coefficients:

Value Std. Error t value Pr(>|t|)

(Intercept) -0.6042 0.1279 -4.7227 0.0000

Ck -0.0472 0.0040 -11.6735 0.0000

Yr 0.0566 0.0115 4.9032 0.0000

JD 0.0099 0.0013 7.9236 0.0000

JD2 0.0000 0.0000 -6.5426 0.0000

QNxtDy -1.0587 0.0593 -17.8450 0.0000

QAve3 0.4737 0.0976 4.8542 0.0000

WTDy 0.0212 0.0069 3.0744 0.0022

WTAve3 -0.1433 0.0198 -7.2379 0.0000

DDCum -0.0012 0.0001 -16.9327 0.0000

L -0.0344 0.0111 -3.0999 0.0020

Yr:JD -0.0004 0.0001 -3.5182 0.0005

QNxtDy:QAve3 -0.6734 0.0618 -10.9041 0.0000

QAve3:JD 0.0015 0.0007 2.0595 0.0397

WTAve3:JD 0.0013 0.0002 7.5458 0.0000

L:JD 0.0004 0.0001 3.5752 0.0004

Residual standard error: 0.09536 on 1023 degrees of freedom

Multiple R-Squared: 0.8761

F-statistic: 482.3 on 15 and 1023 degrees of freedom, the p-value is 0

My questions are:

1. Why does the final linear model include variables that were
not
in the last step, the model with the lowest AIC?

2. How can I figure out how much variation each of the variables
contributes to the total variation?

3. I would like the basic interpretation of the final model. I
understand the process of how stepwise gets to the final model, but am
confused as to why the variables in the final linear model are
different
than the model with the lowest AIC.

Thanks!!!!

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