X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=doc%2Fregression.texi;h=127ec7164567bce7b2f2d2b6233d5e97ca526c03;hb=refs%2Fheads%2Flexer;hp=2a3368539f4dd46895addb832842232ce9e07ff5;hpb=b648b71a8e7180c976aa66dfe05afcf152a1e19d;p=pspp

diff --git a/doc/regression.texi b/doc/regression.texi
index 2a3368539f..127ec71645 100644
--- 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 n observations of a dependent variable, say
-Y_1,@dots{},Y_n, and n observations of one or more explanatory
-variables. Let X_11, X_12, @dots{}, X_1n denote the n observations of the
-first explanatory variable; X_21,@dots{},X_2n denote the n observations of the
-second explanatory variable; X_k1,@dots{},X_kn denote the n observations of the kth
-explanatory variable.
-
-@item The dependent variable Y has the following relationship to the 
+@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 @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 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.
@@ -44,27 +43,27 @@ 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
+@table @subcmd
 @item ALL
 All of the statistics below.
 @item R
@@ -78,18 +77,20 @@ 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
-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
+model to the active dataset. @pspp{} will store the residuals in a variable
+called @samp{RES1} if no such variable exists, @samp{RES2} if @samp{RES1} 
+already exists,
+@samp{RES3} if @samp{RES1} and @samp{RES2} already exist, etc. It will
+choose the name of
+the variable for the predicted values similarly, but with @samp{PRED} as a
 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 +111,3 @@ list.
 regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 
            /save pred resid /method=enter.
 @end example
-@setfilename ignored