Documentation: Tidy up notes about histogram bin widths

diff --git a/doc/statistics.texi b/doc/statistics.texi
index 7977e3387cc2e0713bbc890d2b1c19000ba7ba77..796fde91d3349fa4799aa979e2691abaac72fff6 100644 (file)
--- a/doc/statistics.texi
+++ b/doc/statistics.texi
@@ -197,9 +197,12 @@ For instance, @subcmd{/NTILES=4} would cause quartiles to be reported.
 The @subcmd{HISTOGRAM} subcommand causes the output to include a histogram for
 each specified numeric variable.  The X axis by default ranges from
 the minimum to the maximum value observed in the data, but the @subcmd{MINIMUM}
-and @subcmd{MAXIMUM} keywords can set an explicit range. The number of
-bins are 2IQR(x)n^-1/3 according to the Freedman-Diaconis rule.  (Note that
-@cmd{EXAMINE} uses a different algorithm to determine bin sizes.)
+and @subcmd{MAXIMUM} keywords can set an explicit range. 
+@footnote{The number of
+bins is chosen according to the Freedman-Diaconis rule:
+@math{2 \times IQR(x)n^{-1/3}}, where @math{IQR(x)} is the interquartile range of @math{x}
+and @math{n} is the number of samples.    Note that
+@cmd{EXAMINE} uses a different algorithm to determine bin sizes.}
 Histograms are not created for string variables.
 
 Specify @subcmd{NORMAL} to superimpose a normal curve on the
@@ -306,10 +309,9 @@ The first three can be used to visualise how closely each cell conforms to a
 normal distribution, whilst the spread vs.@: level plot can be useful to visualise
 how the variance of differs between factors.
 Boxplots will also show you the outliers and extreme values.
-
-@subcmd{HISTOGRAM} uses Sturges' rule to determine the number of
-bins, as approximately 1 + log2(n).  (Note that @cmd{FREQUENCIES} uses a
-different algorithm to find the bin size.)
+@footnote{@subcmd{HISTOGRAM} uses Sturges' rule to determine the number of
+bins, as approximately @math{1 + \log2(n)}, where @math{n} is the number of samples.
+Note that @cmd{FREQUENCIES} uses a different algorithm to find the bin size.}
 
 The @subcmd{SPREADLEVEL} plot displays the interquartile range versus the 
 median.  It takes an optional parameter @var{t}, which specifies how the data