1. Regression Analysis: ln Y versus ln X1, ln X2, ...
* WARNING * ln X2 is highly correlated with other predictors.
* WARNING * ln X3 is highly correlated with other predictors.
The regression equation is
ln Y = 7809376 + 1.08 ln X1 - 5765311 ln X2 + 5765311 ln X3 - 0.126 ln X4
+ 0.0105 ln X5 + 0.0018 ln X6
Predictor Coef SE Coef T P VIF
Constant 7809376 6139883 1.27 0.208
ln X1 1.0754 0.1558 6.90 0.000 463.4
ln X2 -5765311 4532798 -1.27 0.208 4.34652E+17
ln X3 5765311 4532798 1.27 0.208 4.34652E+17
ln X4 -0.1257 0.1235 -1.02 0.313 294.7
ln X5 0.010493 0.006677 1.57 0.121 1.7
ln X6 0.00181 0.01259 0.14 0.886 4.2
S = 0.0149235 R-Sq = 99.7% R-Sq(adj) = 99.7%
Analysis of Variance
Source DF SS MS F P
Regression 6 4.25561 0.70927 3184.70 0.000
Residual Error 61 0.01359 0.00022
Total 67 4.26920
Source DF Seq SS
ln X1 1 4.25416
ln X2 1 0.00051
ln X3 1 0.00022
ln X4 1 0.00016
ln X5 1 0.00056
ln X6 1 0.00000
Unusual Observations
Obs ln X1 ln Y Fit SE Fit Residual St Resid
6 9.51 8.00884 8.04129 0.00286 -0.03245 -2.22R
38 9.13 7.69699 7.66139 0.00353 0.03560 2.46R
48 9.74 8.23929 8.26917 0.00302 -0.02988 -2.04R
57 9.58 8.11890 8.12285 0.01380 -0.00395 -0.69 X
61 9.09 7.59816 7.63574 0.00731 -0.03758 -2.89R
64 9.78 8.29977 8.30188 0.01479 -0.00211 -1.04 X
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.
Durbin-Watson statistic = 1.21483