From 430f3a7cd6d9175a54a9e97fb91d5fc912fcfea4 Mon Sep 17 00:00:00 2001 From: Jason Stover 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 + + * 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 * 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