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REGRESSION /VARIABLES=var_list/DEPENDENT=var_list/STATISTICS={ALL, DEFAULTS, R, COEFF, ANOVA, BCOV, CI[conf]} /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 additional statistics to be displayed.
The following keywords are accepted:

`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.

`CI (`

`conf`)This item is only relevant if COEFF has also been selected. It specifies that the confidence interval for the coefficients should be printed. The optional value

`conf`, which must be in parentheses, is the desired confidence level expressed as a percentage.`ANOVA`

Analysis of variance table for the model.

`BCOV`

The covariance matrix for the estimated model coefficients.

`DEFAULT`

The same as if R, COEFF, and ANOVA had been selected.

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.

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