Imported Upstream version 1.72.0

[platform/upstream/boost.git] / libs / math / doc / html / math_toolkit / stat_tut / weg / st_eg / two_sample_students_t.html

diff --git a/libs/math/doc/html/math_toolkit/stat_tut/weg/st_eg/two_sample_students_t.html b/libs/math/doc/html/math_toolkit/stat_tut/weg/st_eg/two_sample_students_t.html
index a9b59a7..33f270a 100644 (file)
--- a/libs/math/doc/html/math_toolkit/stat_tut/weg/st_eg/two_sample_students_t.html
+++ b/libs/math/doc/html/math_toolkit/stat_tut/weg/st_eg/two_sample_students_t.html
@@ -4,7 +4,7 @@
 <title>Comparing the means of two samples with the Students-t test</title>
 <link rel="stylesheet" href="../../../../math.css" type="text/css">
 <meta name="generator" content="DocBook XSL Stylesheets V1.79.1">
-<link rel="home" href="../../../../index.html" title="Math Toolkit 2.10.0">
+<link rel="home" href="../../../../index.html" title="Math Toolkit 2.11.0">
 <link rel="up" href="../st_eg.html" title="Student's t Distribution Examples">
 <link rel="prev" href="tut_mean_size.html" title="Estimating how large a sample size would have to become in order to give a significant Students-t test result with a single sample test">
 <link rel="next" href="paired_st.html" title="Comparing two paired samples with the Student's t distribution">
@@ -75,9 +75,10 @@
             Our procedure will begin by calculating the t-statistic, assuming equal
             variances the needed formulae are:
           </p>
-<p>
-            <span class="inlinemediaobject"><img src="../../../../../equations/dist_tutorial1.svg"></span>
-          </p>
+<div class="blockquote"><blockquote class="blockquote"><p>
+              <span class="inlinemediaobject"><img src="../../../../../equations/dist_tutorial1.svg"></span>
+
+            </p></blockquote></div>
 <p>
             where Sp is the "pooled" standard deviation of the two samples,
             and <span class="emphasis"><em>v</em></span> is the number of degrees of freedom of the
@@ -260,16 +261,18 @@
             at the more complex one: that the standard deviations of the two samples
             are not equal. In this case the formula for the t-statistic becomes:
           </p>
-<p>
-            <span class="inlinemediaobject"><img src="../../../../../equations/dist_tutorial2.svg"></span>
-          </p>
+<div class="blockquote"><blockquote class="blockquote"><p>
+              <span class="inlinemediaobject"><img src="../../../../../equations/dist_tutorial2.svg"></span>
+
+            </p></blockquote></div>
 <p>
             And for the combined degrees of freedom we use the <a href="http://en.wikipedia.org/wiki/Welch-Satterthwaite_equation" target="_top">Welch-Satterthwaite</a>
             approximation:
           </p>
-<p>
-            <span class="inlinemediaobject"><img src="../../../../../equations/dist_tutorial3.svg"></span>
-          </p>
+<div class="blockquote"><blockquote class="blockquote"><p>
+              <span class="inlinemediaobject"><img src="../../../../../equations/dist_tutorial3.svg"></span>
+
+            </p></blockquote></div>
 <p>
             Note that this is one of the rare situations where the degrees-of-freedom
             parameter to the Student's t distribution is a real number, and not an