Imported Upstream version 1.72.0

[platform/upstream/boost.git] / libs / math / doc / distributions / students_t_examples.qbk

diff --git a/libs/math/doc/distributions/students_t_examples.qbk b/libs/math/doc/distributions/students_t_examples.qbk
index 2e716a5..1204dc0 100644 (file)
--- a/libs/math/doc/distributions/students_t_examples.qbk
+++ b/libs/math/doc/distributions/students_t_examples.qbk
@@ -21,7 +21,7 @@ Where, ['Y[sub s]] is the sample mean, /s/ is the sample standard deviation,
 distribution with /N-1/ degrees of freedom.
 
 [note
-The quantity [alpha][space] is the maximum acceptable risk of falsely rejecting
+The quantity [alpha] is the maximum acceptable risk of falsely rejecting
 the null-hypothesis.  The smaller the value of [alpha] the greater the
 strength of the test.
 
@@ -88,7 +88,7 @@ Note that these are the complements of the confidence/probability levels: 0.5, 0
 Next we'll declare the distribution object we'll need, note that
 the /degrees of freedom/ parameter is the sample size less one:
 
-      students_t dist(Sn - 1);
+  students_t dist(Sn - 1);
 
 Most of what follows in the program is pretty printing, so let's focus
 on the calculation of the interval. First we need the t-statistic,
@@ -186,7 +186,7 @@ This time the fact that there are only three measurements leads to
 much wider intervals, indeed such large intervals that it's hard
 to be very confident in the location of the mean.
 
-[endsect]
+[endsect] [/section:tut_mean_intervals Calculating confidence intervals on the mean with the Students-t distribution]
 
 [section:tut_mean_test Testing a sample mean for difference from a "true" mean]
 
@@ -394,7 +394,8 @@ In this case, we really have a borderline result,
 and more data (and/or more accurate data),
 is needed for a more convincing conclusion.
 
-[endsect]
+[endsect] [/section:tut_mean_test Testing a sample mean for difference from a "true" mean]
+
 
 [section:tut_mean_size 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]
@@ -521,7 +522,8 @@ _______________________________________________________________
 So in this case, many more measurements would have had to be made,
 for example at the 95% level, 14 measurements in total for a two-sided test.
 
-[endsect]
+[endsect] [/section:tut_mean_size 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]
+
 [section:two_sample_students_t Comparing the means of two samples with the Students-t test]
 
 Imagine that we have two samples, and we wish to determine whether
@@ -744,7 +746,8 @@ a higher likelihood that the observed difference is down to chance alone
 However, the conclusion remains the same: US cars are less fuel efficient
 than Japanese models.
 
-[endsect]
+[endsect] [/section:two_sample_students_t Comparing the means of two samples with the Students-t test]
+
 [section:paired_st Comparing two paired samples with the Student's t distribution]
 
 Imagine that we have a before and after reading for each item in the sample:
@@ -768,9 +771,10 @@ null-hypothesis that there is no change.
 * [link math_toolkit.stat_tut.weg.st_eg.tut_mean_size Calculate how many pairs of readings we would need
 in order to obtain a significant result].
 
-[endsect]
+[endsect] [/section:paired_st Comparing two paired samples with the Student's t distribution]
+
 
-[endsect][/section:st_eg Student's t]
+[endsect] [/section:st_eg Student's t]
 
 [/
   Copyright 2006, 2012 John Maddock and Paul A. Bristow.