From 430f3a7cd6d9175a54a9e97fb91d5fc912fcfea4 Mon Sep 17 00:00:00 2001
From: Jason Stover <jhs@math.gcsu.edu>
Date: Tue, 11 Mar 2008 21:14:29 +0000
Subject: [PATCH] Use math mode more consistently. Mention 0 mean of the error
terms.
---
doc/ChangeLog | 6 ++++++
doc/regression.texi | 14 +++++++-------
2 files changed, 13 insertions(+), 7 deletions(-)
diff --git a/doc/ChangeLog b/doc/ChangeLog
index fdc3c8e7..adc7d20c 100644
--- a/doc/ChangeLog
+++ b/doc/ChangeLog
@@ -1,3 +1,9 @@
+2008-03-11 Jason Stover <jhs@math.gcsu.edu>
+
+ * regression.texi: Made more consistent use of math mode for
+ description of linear regression. Added reference to the mean of
+ the error terms being 0.
+
2008-03-09 Jason Stover <jhs@math.gcsu.edu>
* regression.texi (REGRESSION): Removed references to subcommand EXPORT.
diff --git a/doc/regression.texi b/doc/regression.texi
index 2a336853..d07b513a 100644
--- a/doc/regression.texi
+++ b/doc/regression.texi
@@ -9,19 +9,19 @@ 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
+@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{}, X_{1n}} denote the @math{n} observations of the
+first explanatory variable; @math{X_{21},@dots{},X_{2n}} denote the @math{n} observations of the
+second explanatory variable; @math{X_{k1},@dots{},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, @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
+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
--
2.30.2