Multiple Regression Analysis
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Dependent variable: AUTOADJ/CPI
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                                       Standard          T
Parameter               Estimate         Error       Statistic        P-Value
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CONSTANT                 1.40164       0.709646        1.97513         0.0492
INCOME/CPI             0.0782192      0.0028257        27.6814         0.0000
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                           Analysis of Variance
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Source             Sum of Squares     Df  Mean Square    F-Ratio      P-Value
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Model                     3142.94      1      3142.94     766.26       0.0000
Residual                  1173.08    286      4.10166
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Total (Corr.)             4316.02    287

R-squared = 72.8204 percent
R-squared (adjusted for d.f.) = 72.7254 percent
Standard Error of Est. = 2.02526
Mean absolute error = 1.64644
Durbin-Watson statistic = 0.449251

The StatAdvisor
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   The output shows the results of fitting a multiple linear
regression model to describe the relationship between AUTOADJ/CPI and
1 independent variables.  The equation of the fitted model is

AUTOADJ/CPI = 1.40164 + 0.0782192*INCOME/CPI

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 72.8204% of the variability in AUTOADJ/CPI.  The adjusted
R-squared statistic, which is more suitable for comparing models with
different numbers of independent variables, is 72.7254%.  The standard
error of the estimate shows the standard deviation of the residuals to
be 2.02526.  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 1.64644 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
INCOME/CPI.  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.