X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=doc%2Fregression.texi;h=b47416b70191c1a1af0129ca073a36fa7a390056;hb=HEAD;hp=af3c0aaf244b940f45b7cd5e276db0ac4636181a;hpb=85cef9b9391f1aaf4b27bd90a02f8ce5daf004ee;p=pspp-builds.git

diff --git a/doc/regression.texi b/doc/regression.texi
index af3c0aaf..b47416b7 100644
--- a/doc/regression.texi
+++ b/doc/regression.texi
@@ -1,4 +1,4 @@
-@node REGRESSION, ,RANK, Statistics
+@node REGRESSION
 @comment  node-name,  next,  previous,  up
 @section REGRESSION
 
@@ -9,25 +9,25 @@ 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,...,Y_n, and n observations of one or more explanatory
-variables. Let X_11, X_12, ..., X_1n denote the n observations of the
-first explanatory variable; X_21,...,X_2n denote the n observations of the
-second explanatory variable; X_k1,...,X_kn denote the n observations of the kth
+@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.
 
-@item The dependent variable Y has the following relationship to the 
+@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, ..., b_k} are unknown
-coefficients, and @math{Z_1,...,Z_n} are independent, normally
-distributed ``noise'' terms with common variance. The noise, or
+@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.''
 @end itemize
 
 The REGRESSION procedure estimates the coefficients
-@math{b_0,...,b_k} and produces output relevant to inferences for the
+@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 
@@ -38,7 +38,7 @@ linear model.
 * Examples::                    Using the REGRESSION procedure.
 @end menu
 
-@node Syntax, Examples, , REGRESSION
+@node Syntax
 @subsection Syntax
 
 @vindex REGRESSION
@@ -47,7 +47,6 @@ REGRESSION
         /VARIABLES=var_list
         /DEPENDENT=var_list
         /STATISTICS=@{ALL, DEFAULTS, R, COEFF, ANOVA, BCOV@}
-        /EXPORT ('file-name')
         /SAVE=@{PRED, RESID@}
 @end display
 
@@ -87,23 +86,10 @@ 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
-explanatory variables as arguments, and returns an estimate of the
-corresponding new
-value of the dependent variable. The generated program will also contain
-functions that return prediction and confidence intervals related to
-those new estimates. PSPP will write the program to the
-'file-name' given by the user, and write declarations of functions
-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 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.''
+The following PSPP syntax will generate the default output and save the
+predicted values and residuals to the active file.
 
 @example
 title 'Demonstrate REGRESSION procedure'.
@@ -122,19 +108,5 @@ b  6.200189 -18.58219
 end data.
 list.
 regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 
-           /export (model.c) /save pred resid /method=enter.
+           /save pred resid /method=enter.
 @end example
-
-The file pspp_model_reg.h contains these declarations:
-
-@example
-double pspp_reg_estimate (const double *, const char *[]);
-double pspp_reg_variance (const double *var_vals, const char *[]);
-double pspp_reg_confidence_interval_U (const double *var_vals, const char *var_names[], double p);
-double pspp_reg_confidence_interval_L (const double *var_vals, const char *var_names[], double p);
-double pspp_reg_prediction_interval_U (const double *var_vals, const char *var_names[], double p);
-double pspp_reg_prediction_interval_L (const double *var_vals, const char *var_names[], double p);
-@end example
-
-The file model.c contains the definitions of the functions. 
-@setfilename ignored