Independent variables. The equation of the fitted model is

L = 64,3379*P/LAG(P;1) + 1,85027*time

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 99,9719% of the variability in L. The adjusted R-squared

statistic, which is more suitable for comparing models with different

numbers of independent variables, is 99,9709%. (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 1,72078.

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 1,36569 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 0,0000, belonging to

P/LAG(P;1). Since the P-value is less than 0.01, the highest order

term is statistically significant at the 99% confidence level.

Consequently, you probably don't want to remove any variables from the

model.

Multiple Regression Analysis I.1

-----------------------------------------------------------------------------

Dependent variable: I

-----------------------------------------------------------------------------

Standard T

Parameter Estimate Error Statistic P-Value

-----------------------------------------------------------------------------

Tr 33,1236 15,6678 2,11412 0,0456

Irr 0,253029 0,197316 1,28235 0,2125

K 0,802794 0,026699 30,0683 0,0000

LAG(K;1) -0,749459 0,0267706 -27,9956 0,0000

Y 0,102871 0,0143734 7,15708 0,0000

LAG(Y;1) -0,0903003 0,0140434 -6,43008 0,0000

-----------------------------------------------------------------------------

Analysis of Variance

-----------------------------------------------------------------------------

Source Sum of Squares Df Mean Square F-Ratio P-Value

-----------------------------------------------------------------------------

Model 1,01524E7 6 1,69207E6 143333,58 0,0000

Residual 271,519 23 11,8052

-----------------------------------------------------------------------------

Total 1,01527E7 29

R-squared = 99,9973 percent

R-squared (adjusted for d.f.) = 99,9967 percent

Standard Error of Est. = 3,43586

Mean absolute error = 2,46358

Durbin-Watson statistic = 2,79613

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 6