Independent variables. The equation of the fitted model is

Y = 23,4205*Irr + 0,544344*Yl - 0,280307*Kl + 20,4968*Ll + 15,7494*Pl

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 99,9005% of the variability in Y. The adjusted R-squared

statistic, which is more suitable for comparing models with different

numbers of independent variables, is 99,8832%. (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 126,857.

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 90,5341 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 0,0475, belonging to

Kl. Since the P-value is less than 0.05, that term is statistically

significant at the 95% confidence level. Consequently, you probably

don't want to remove any variables from the model.

Multiple Regression Analysis 2MNK1 L

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

Dependent variable: L

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

Standard T

Parameter Estimate Error Statistic P-Value

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

CONSTANT 127,376 17,0496 7,47089 0,0000

Yl 0,021109 0,00529585 3,98595 0,0006

Kl -0,0374232 0,0100228 -3,7338 0,0011

Pl 0,251053 0,0733584 3,42229 0,0023

time 6,66227 1,36668 4,87479 0,0001

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

Analysis of Variance

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

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

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

Model 6261,01 4 1565,25 680,88 0,0000

Residual 52,8743 23 2,29888

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

Total (Corr.) 6313,89 27

R-squared = 99,1626 percent

R-squared (adjusted for d.f.) = 99,0169 percent

Standard Error of Est. = 1,51621

Mean absolute error = 1,13788

Durbin-Watson statistic = 2,41022

The StatAdvisor

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The output shows the results of fitting a multiple linear

regression model to describe the relationship between L and 4