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[pspp-builds.git] / doc / regression.texi

diff --git a/doc/regression.texi b/doc/regression.texi
index 22b9f58ff664c196a574565aeac548983f452f1f..8b6d7e4d70f268bd3a83fc6b1ddf5393d57a6a8a 100644 (file)
--- a/doc/regression.texi
+++ b/doc/regression.texi
@@ -1,6 +1,9 @@
-@node REGRESSION, , ONEWAY, Statistics
+@node REGRESSION
+@comment  node-name,  next,  previous,  up
 @section REGRESSION
 
+@cindex regression
+@cindex linear regression
 The REGRESSION procedure fits linear models to data via least-squares
 estimation. The procedure is appropriate for data which satisfy those
 assumptions typical in linear regression:
@@ -35,7 +38,7 @@ linear model.
 * Examples::                    Using the REGRESSION procedure.
 @end menu
 
-@node Syntax, Examples, , REGRESSION
+@node Syntax
 @subsection Syntax
 
 @vindex REGRESSION
@@ -45,7 +48,7 @@ REGRESSION
         /DEPENDENT=var_list
         /STATISTICS=@{ALL, DEFAULTS, R, COEFF, ANOVA, BCOV@}
         /EXPORT ('file-name')
-        /SAVE
+        /SAVE=@{PRED, RESID@}
 @end display
 
 The @cmd{REGRESSION} procedure reads the active file and outputs
@@ -54,7 +57,7 @@ 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 subcommond is required. All variables listed in
+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.
 
@@ -76,10 +79,13 @@ Analysis of variance table for the model.
 The covariance matrix for the estimated model coefficients.
 @end table
 
-The SAVE subcommand causes PSPP to save the residuals from the fitted
+The SAVE subcommand causes PSPP to save the residuals or predicted
+values from the fitted
 model to the active file. 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.
+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.
 
 The EXPORT subcommand causes PSPP to write a C program containing
 functions related to the model. One such function accepts values of
@@ -93,9 +99,10 @@ to a file called pspp_model_reg.h. The user can then compile the C
 program and use it as part of another program. This subcommand is a
 PSPP extension.
 
-@node Examples, , Syntax, REGRESSION
+@node Examples
 @subsection Examples
-The following PSPP code will generate the default output, and save the
+The following PSPP syntax will generate the default output, save the
+predicted values and residuals to the active file, and save the
 linear model in a program called ``model.c.''
 
 @example
@@ -114,7 +121,8 @@ a  8.838262 -29.25689
 b  6.200189 -18.58219
 end data.
 list.
-regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 /export (model.c) /method=enter.
+regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 
+           /export (model.c) /save pred resid /method=enter.
 @end example
 
 The file pspp_model_reg.h contains these declarations: