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Independent variables. The equation of the fitted model is
I = 33,1236*Tr + 0,253029*Irr + 0,802794*K - 0,749459*LAG(K;1) +
0,102871*Y - 0,0903003*LAG(Y;1)
Since the P-value in the ANOVA table is less than 0.01, there is a
statistically significant relationship between the variables at the
99% Confidence level.
The R-Squared statistic indicates that the model as fitted
explains 99,9973% of the variability in I. The adjusted R-squared
statistic, which is more suitable for comparing models with different
numbers of independent variables, is 99,9967%. (Note: since the model
does not contain a constant, you should be careful in interpreting the
R-Squared values. Do not compare these R-Squared values with those of
models which do contain a constant.) The standard error of the
estimate shows the standard deviation of the residuals to be 3,43586.
This value can be used to construct prediction limits for new
observations by selecting the Reports option from the text menu. The
mean absolute error (MAE) of 2,46358 is the average value of the
residuals. The Durbin-Watson (DW) statistic tests the residuals to
determine if there is any significant correlation based on the order
in which they occur in your data file. Since the DW value is greater
than 1.4, there is probably not any serious autocorrelation in the
residuals.
In determining whether the model can be simplified, notice that the
highest P-value on the independent variables is 0,2125, belonging to
Irr. Since the P-value is greater or equal to 0.10, that term is not
statistically significant at the 90% or higher confidence level.
Consequently, you should consider removing Irr from the model.
Multiple Regression Analysis I.2
-----------------------------------------------------------------------------
Dependent variable: I
-----------------------------------------------------------------------------
Standard T
Parameter Estimate Error Statistic P-Value
-----------------------------------------------------------------------------
Tr 33,3577 15,8757 2,10118 0,0463
K 0,800313 0,0269839 29,6589 0,0000
LAG(K;1) -0,748868 0,0271236 -27,6094 0,0000
Y 0,105549 0,0144106 7,3244 0,0000
LAG(Y;1) -0,0890858 0,0141983 -6,27439 0,0000
-----------------------------------------------------------------------------
Analysis of Variance
-----------------------------------------------------------------------------
Source Sum of Squares Df Mean Square F-Ratio P-Value
-----------------------------------------------------------------------------
Model 1,01524E7 5 2,03049E6 167502,37 0,0000
Residual 290,931 24 12,1221
-----------------------------------------------------------------------------
Total 1,01527E7 29
R-squared = 99,9971 percent
R-squared (adjusted for d.f.) = 99,9967 percent
Standard Error of Est. = 3,48169
Mean absolute error = 2,53173
Durbin-Watson statistic = 2,69255
The StatAdvisor
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The output shows the results of fitting a multiple linear
regression model to describe the relationship between I and 5