Change terminology from "active file" to "active dataset".

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

diff --git a/doc/regression.texi b/doc/regression.texi
index 66125b48da25519544cc3bf4724978f0da8d0466..9885d8444a8335468c64c5f6079a5945919a5d29 100644 (file)
--- a/doc/regression.texi
+++ b/doc/regression.texi
@@ -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
+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 
@@ -50,7 +50,7 @@ REGRESSION
         /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
@@ -80,7 +80,7 @@ The covariance matrix for the estimated model coefficients.
 
 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
+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
@@ -89,7 +89,7 @@ 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.
+predicted values and residuals to the active dataset.
 
 @example
 title 'Demonstrate REGRESSION procedure'.
@@ -110,4 +110,3 @@ list.
 regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 
            /save pred resid /method=enter.
 @end example
-@setfilename ignored