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.
When `SAVE`

is used, pspp ignores `TEMPORARY`

, treating
temporary transformations as permanent.