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Independent variables. The equation of the fitted model is
P = 0,0134573*LAG(Y;1) + 0,120143*LAG(M;1) - 13,631*W + 0,224049*G
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 99,3926% of the variability in P. The adjusted R-squared
statistic, which is more suitable for comparing models with different
numbers of independent variables, is 99,3197%. (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 7,94415.
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 6,00654 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 less
than 1.4, there may be some indication of serial correlation. Plot
the residuals versus row order to see if there is any pattern which
can be seen.
In determining whether the model can be simplified, notice that the
highest P-value on the independent variables is 0,0518, belonging to
LAG(Y;1). Since the P-value is less than 0.10, that term is
statistically significant at the 90% confidence level. Depending on
the confidence level at which you wish to work, you may or may not
decide to remove LAG(Y;1) from the model.
Multiple Regression Analysis P.6
-----------------------------------------------------------------------------
Dependent variable: LOG(P)
-----------------------------------------------------------------------------
Standard T
Parameter Estimate Error Statistic P-Value
-----------------------------------------------------------------------------
CONSTANT 5,92527 0,552303 10,7283 0,0000
time*LOG(C) 0,00796706 0,000124393 64,0473 0,0000
LOG(LAG(M;1)) -0,198376 0,0580044 -3,42001 0,0025
LOG(W) -0,512978 0,126723 -4,04802 0,0005
-----------------------------------------------------------------------------
Analysis of Variance
-----------------------------------------------------------------------------
Source Sum of Squares Df Mean Square F-Ratio P-Value
-----------------------------------------------------------------------------
Model 5,20282 3 1,73427 10205,77 0,0000
Residual 0,00373847 22 0,000169931
-----------------------------------------------------------------------------
Total (Corr.) 5,20655 25
R-squared = 99,9282 percent
R-squared (adjusted for d.f.) = 99,9184 percent
Standard Error of Est. = 0,0130357
Mean absolute error = 0,0102107
Durbin-Watson statistic = 1,19364
The StatAdvisor
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The output shows the results of fitting a multiple linear
regression model to describe the relationship between LOG(P) and 3