99% Confidence level.

The R-Squared statistic indicates that the model as fitted

explains 99,4721% of the variability in Y. The adjusted R-squared

statistic, which is more suitable for comparing models with different

numbers of independent variables, is 99,4721%. (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 266,387.

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 237,73 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 0,0000, belonging to

Kpr. 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 2MNK2.L

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

Dependent variable: L

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

Standard T

Parameter Estimate Error Statistic P-Value

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

Ppr/LAG(Ppr;1) 64,9814 0,893671 72,7129 0,0000

time 1,81746 0,0506937 35,8518 0,0000

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

Analysis of Variance

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

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

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

Model 274521,0 2 137261,0 32765,67 0,0000

Residual 104,729 25 4,18916

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

Total 274626,0 27

R-squared = 99,9619 percent

R-squared (adjusted for d.f.) = 99,9603 percent

Standard Error of Est. = 2,04674

Mean absolute error = 1,70969

Durbin-Watson statistic = 2,0248

The StatAdvisor

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

The output shows the results of fitting a multiple linear

regression model to describe the relationship between L and 2