Examine: Added the /PLOT=SPREADLEVEL option

[pspp] / doc / regression.texi

diff --git a/doc/regression.texi b/doc/regression.texi
index 90caf866d5f786a52c1be4ef99d01bb31b665539..e362c7fef139a3df806109729903a6c6437a5e61 100644 (file)
--- a/doc/regression.texi
+++ b/doc/regression.texi
@@ -1,38 +1,37 @@
 @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
+The @cmd{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:
 
 @itemize @bullet
 @item The data set contains @math{n} observations of a dependent variable, say
 @math{Y_1,@dots{},Y_n}, and @math{n} observations of one or more explanatory
-variables. Let @math{X_{11}, X_{12}}, @dots{}, @math{X_{1n}} denote the @math{n} observations of the
-first explanatory variable; @math{X_{21}},@dots{},@math{X_{2n}} denote the @math{n} observations of the
-second explanatory variable; @math{X_{k1}},@dots{},@math{X_{kn}} denote the @math{n} observations of the kth
-explanatory variable.
+variables.
+Let @math{X_{11}, X_{12}}, @dots{}, @math{X_{1n}} denote the @math{n} observations
+of the first explanatory variable;
+@math{X_{21}},@dots{},@math{X_{2n}} denote the @math{n} observations of the second
+explanatory variable;
+@math{X_{k1}},@dots{},@math{X_{kn}} denote the @math{n} observations of 
+the @math{k}th explanatory variable.
 
 @item The dependent variable @math{Y} has the following relationship to the 
 explanatory variables:
 @math{Y_i = b_0 + b_1 X_{1i} + ... + b_k X_{ki} + Z_i} 
 where @math{b_0, b_1, @dots{}, b_k} are unknown
 coefficients, and @math{Z_1,@dots{},Z_n} are independent, normally
-distributed ``noise'' terms with mean zero and common variance. The noise, or
-``error'' terms are unobserved. This relationship is called the
-``linear model.''
+distributed @dfn{noise} terms with mean zero and common variance.
+The noise, or @dfn{error} terms are unobserved.
+This relationship is called the @dfn{linear model}.
 @end itemize
 
-The REGRESSION procedure estimates the coefficients
+The @cmd{REGRESSION} procedure estimates the coefficients
 @math{b_0,@dots{},b_k} and produces output relevant to inferences for the
 linear model. 
 
-@c If you add any new commands, then don't forget to remove the entry in 
-@c not-implemented.texi
-
 @menu
 * Syntax::                      Syntax definition.
 * Examples::                    Using the REGRESSION procedure.
@@ -44,25 +43,25 @@ linear model.
 @vindex REGRESSION
 @display
 REGRESSION
-        /VARIABLES=var_list
-        /DEPENDENT=var_list
+        /VARIABLES=@var{var_list}
+        /DEPENDENT=@var{var_list}
         /STATISTICS=@{ALL, DEFAULTS, R, COEFF, ANOVA, BCOV@}
         /SAVE=@{PRED, RESID@}
 @end display
 
-The @cmd{REGRESSION} procedure reads the active file and outputs
+The @cmd{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,
+The @subcmd{VARIABLES} subcommand, which is required, specifies the list of
+variables to be analyzed.  Keyword @subcmd{VARIABLES} is required. The
+@subcmd{DEPENDENT} subcommand specifies the dependent variable of the linear
+model. The @subcmd{DEPENDENT} subcommand is required. All variables listed in
+the @subcmd{VARIABLES} subcommand, but not listed in the @subcmd{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:
+The @subcmd{STATISTICS} subcommand specifies the statistics to be displayed:
 
 @table @code
 @item ALL
@@ -78,9 +77,9 @@ 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 or predicted
+The @subcmd{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
+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
@@ -88,8 +87,8 @@ prefix.
 
 @node Examples
 @subsection Examples
-The following PSPP syntax will generate the default output and save the
-predicted values and residuals to the active file.
+The following @pspp{} syntax will generate the default output and save the
+predicted values and residuals to the active dataset.
 
 @example
 title 'Demonstrate REGRESSION procedure'.
@@ -110,4 +109,3 @@ list.
 regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 
            /save pred resid /method=enter.
 @end example
-@setfilename ignored