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REGRESSION /VARIABLES=var_list /DEPENDENT=var_list /STATISTICS={ALL, DEFAULTS, R, COEFF, ANOVA, BCOV} /SAVE={PRED, RESID}
The REGRESSION
procedure reads the active dataset and outputs
statistics relevant to the linear model specified by the user.
The VARIABLES
subcommand, which is required, specifies the list of
variables to be analyzed. Keyword VARIABLES
is required. The
DEPENDENT
subcommand specifies the dependent variable of the linear
model. The DEPENDENT
subcommand is required. All variables listed in
the VARIABLES
subcommand, but not listed in the DEPENDENT
subcommand,
are treated as explanatory variables in the linear model.
All other subcommands are optional:
The STATISTICS
subcommand specifies the statistics to be displayed:
ALL
All of the statistics below.
R
The ratio of the sums of squares due to the model to the total sums of squares for the dependent variable.
COEFF
A table containing the estimated model coefficients and their standard errors.
ANOVA
Analysis of variance table for the model.
BCOV
The covariance matrix for the estimated model coefficients.
The SAVE
subcommand causes PSPP to save the residuals or predicted
values from the fitted
model to the active dataset. PSPP will store the residuals in a variable
called ‘RES1’ if no such variable exists, ‘RES2’ if ‘RES1’
already exists,
‘RES3’ if ‘RES1’ and ‘RES2’ already exist, etc. It will
choose the name of
the variable for the predicted values similarly, but with ‘PRED’ as a
prefix.