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Science Forum Index » Statistics - Education Forum » stepwise regression, splus, results interpretation
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| Author |
Message |
| jf |
 Posted: Wed Feb 07, 2007 6:22 pm |
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Guest
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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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