99% Confidence level.

The R-Squared statistic indicates that the model as fitted

explains 99,7397% of the variability in Y. The adjusted R-squared

statistic, which is more suitable for comparing models with different

numbers of independent variables, is 99,7304%. (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 188,179.

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 142,529 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 0,0004, belonging to

L. 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 Y.4

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

Dependent variable: LOG(Y)

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

Standard T

Parameter Estimate Error Statistic P-Value

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

LOG(K) 1,48893 0,244271 6,09539 0,0000

LOG(L) -1,05751 0,465343 -2,27253 0,0309

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

Analysis of Variance

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

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

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

Model 1975,71 2 987,857 337939,07 0,0000

Residual 0,081849 28 0,00292318

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

Total 1975,8 30

R-squared = 99,9959 percent

R-squared (adjusted for d.f.) = 99,9957 percent

Standard Error of Est. = 0,0540664

Mean absolute error = 0,0470633

Durbin-Watson statistic = 0,273538

The StatAdvisor

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The output shows the results of fitting a multiple linear

regression model to describe the relationship between LOG(Y) and 2