99% Confidence level.

The R-Squared statistic indicates that the model as fitted

explains 92,1503% of the variability in M. The adjusted R-squared

statistic, which is more suitable for comparing models with different

numbers of independent variables, is 91,8484%. The standard error of

the estimate shows the standard deviation of the residuals to be

23,2495. 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 18,7576 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

Ypr*Ppr. 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.P

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

Dependent variable: LOG(P)

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

Standard T

Parameter Estimate Error Statistic P-Value

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

CONSTANT 5,33115 0,361535 14,7459 0,0000

time*LOG(Cpr) 0,00784084 0,00010377 75,5598 0,0000

LOG(LAG(Mpr;1)) -0,176665 0,0657341 -2,68757 0,0131

LOG(Wpr) -0,332081 0,109375 -3,03616 0,0059

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

Analysis of Variance

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

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

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

Model 5,9364 3 1,9788 9565,90 0,0000

Residual 0,00475777 23 0,00020686

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

Total (Corr.) 5,94115 26

R-squared = 99,9199 percent

R-squared (adjusted for d.f.) = 99,9095 percent

Standard Error of Est. = 0,0143826

Mean absolute error = 0,0110333

Durbin-Watson statistic = 0,762942

The StatAdvisor

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

The output shows the results of fitting a multiple linear

regression model to describe the relationship between LOG(P) and 3

Independent variables. The equation of the fitted model is

LOG(P) = 5,33115 + 0,00784084*time*LOG(Cpr) - 0,176665*LOG(LAG(Mpr;1))

- 0,332081*LOG(Wpr)

Since the P-value in the ANOVA table is less than 0.01, there is a

statistically significant relationship between the variables at the