Next: Examples, Up: REGRESSION [Contents][Index]
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:
ALLAll of the statistics below.
RThe ratio of the sums of squares due to the model to the total sums of squares for the dependent variable.
COEFFA table containing the estimated model coefficients and their standard errors.
ANOVAAnalysis of variance table for the model.
BCOVThe 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.