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Multiple Regression Analysis Y.1
-----------------------------------------------------------------------------
Dependent variable: Y
-----------------------------------------------------------------------------
Standard T
Parameter Estimate Error Statistic P-Value
-----------------------------------------------------------------------------
K 1,13197 0,120403 9,40153 0,0000
L -19,5968 5,08977 -3,85022 0,0007
time -108,127 17,0596 -6,33818 0,0000
-----------------------------------------------------------------------------
Analysis of Variance
-----------------------------------------------------------------------------
Source Sum of Squares Df Mean Square F-Ratio P-Value
-----------------------------------------------------------------------------
Model 3,80506E8 3 1,26835E8 8592,71 0,0000
Residual 398541,0 27 14760,8
-----------------------------------------------------------------------------
Total 3,80904E8 30
R-squared = 99,8954 percent
R-squared (adjusted for d.f.) = 99,8876 percent
Standard Error of Est. = 121,494
Mean absolute error = 100,802
Durbin-Watson statistic = 0,377205
The StatAdvisor
---------------
The output shows the results of fitting a multiple linear
regression model to describe the relationship between Y and 3
independent variables. The equation of the fitted model is
Y = 1,13197*K - 19,5968*L - 108,127*time
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 99,8954% of the variability in Y. The adjusted R-squared
statistic, which is more suitable for comparing models with different
numbers of independent variables, is 99,8876%. (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 121,494.
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 100,802 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 0,0007, 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.2
-----------------------------------------------------------------------------
Dependent variable: Y
-----------------------------------------------------------------------------
Standard T
Parameter Estimate Error Statistic P-Value
-----------------------------------------------------------------------------
CONSTANT 1771,93 588,957 3,00859 0,0056
K 0,716101 0,115047 6,2244 0,0000
L -28,5746 13,2824 -2,15131 0,0406
-----------------------------------------------------------------------------
Analysis of Variance
-----------------------------------------------------------------------------
Source Sum of Squares Df Mean Square F-Ratio P-Value
-----------------------------------------------------------------------------
Model 2,36484E7 2 1,18242E7 429,93 0,0000
Residual 742576,0 27 27502,8
-----------------------------------------------------------------------------
Total (Corr.) 2,4391E7 29
R-squared = 96,9555 percent
R-squared (adjusted for d.f.) = 96,73 percent
Standard Error of Est. = 165,84
Mean absolute error = 129,964
Durbin-Watson statistic = 0,40917
The StatAdvisor
---------------
The output shows the results of fitting a multiple linear
regression model to describe the relationship between Y and 2
Independent variables. The equation of the fitted model is
Y = 1771,93 + 0,716101*K - 28,5746*L
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 96,9555% of the variability in Y. The adjusted R-squared
statistic, which is more suitable for comparing models with different
numbers of independent variables, is 96,73%. The standard error of
the estimate shows the standard deviation of the residuals to be
165,84. 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 129,964 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 0,0406, belonging to
L. 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 Y.3
-----------------------------------------------------------------------------
Dependent variable: Y
-----------------------------------------------------------------------------
Standard T
Parameter Estimate Error Statistic P-Value
-----------------------------------------------------------------------------
K 0,387031 0,0404787 9,56135 0,0000
L 10,7536 2,672 4,02457 0,0004
-----------------------------------------------------------------------------
Analysis of Variance
-----------------------------------------------------------------------------
Source Sum of Squares Df Mean Square F-Ratio P-Value
-----------------------------------------------------------------------------
Model 3,79913E8 2 1,89956E8 5364,27 0,0000
Residual 991520,0 28 35411,4
-----------------------------------------------------------------------------
Total 3,80904E8 30
R-squared = 99,7397 percent
R-squared (adjusted for d.f.) = 99,7304 percent
Standard Error of Est. = 188,179
Mean absolute error = 142,529
Durbin-Watson statistic = 0,628631
The StatAdvisor
---------------
The output shows the results of fitting a multiple linear
regression model to describe the relationship between Y and 2
Independent variables. The equation of the fitted model is
Y = 0,387031*K + 10,7536*L
Since the P-value in the ANOVA table is less than 0.01, there is a
statistically significant relationship between the variables at the