🚀 go-pugleaf

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!!!!