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Independent variables. The equation of the fitted model is
M = 486,643 + 0,000310609*Y*P
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 92,6056% of the variability in M. The adjusted R-squared
statistic, which is more suitable for comparing models with different
numbers of independent variables, is 92,3415%. The standard error of
the estimate shows the standard deviation of the residuals to be
22,095. 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 17,6083 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
Y*P. 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 P.1
-----------------------------------------------------------------------------
Dependent variable: P
-----------------------------------------------------------------------------
Standard T
Parameter Estimate Error Statistic P-Value
-----------------------------------------------------------------------------
Y 0,013878 0,00495595 2,80028 0,0095
M 0,122215 0,0342687 3,56639 0,0014
W -13,0803 1,19768 -10,9214 0,0000
G 0,205698 0,0266377 7,72206 0,0000
-----------------------------------------------------------------------------
Analysis of Variance
-----------------------------------------------------------------------------
Source Sum of Squares Df Mean Square F-Ratio P-Value
-----------------------------------------------------------------------------
Model 259702,0 4 64925,4 1628,90 0,0000
Residual 1036,32 26 39,8585
-----------------------------------------------------------------------------
Total 260738,0 30
R-squared = 99,6025 percent
R-squared (adjusted for d.f.) = 99,5567 percent
Standard Error of Est. = 6,31336
Mean absolute error = 4,95284
Durbin-Watson statistic = 1,06157
The StatAdvisor
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The output shows the results of fitting a multiple linear
regression model to describe the relationship between P and 4