Small wording fixups.

1 files changed, 5 insertions, 5 deletions

diff --git a/Doc/lib/libdecimal.tex b/Doc/lib/libdecimal.tex
index 11daf71..e982826 100644
--- a/Doc/lib/libdecimal.tex
+++ b/Doc/lib/libdecimal.tex
@@ -839,7 +839,7 @@ fixed precision.
 The effects of round-off error can be amplified by the addition or subtraction
 of nearly offsetting quantities resulting in loss of significance.  Knuth
 provides two instructive examples where rounded floating point arithmetic with
-insufficient precision causes the break down of the associative and
+insufficient precision causes the breakdown of the associative and
 distributive properties of addition:
 
 \begin{verbatim}
@@ -893,7 +893,7 @@ The infinities are signed (affine) and can be used in arithmetic operations
 where they get treated as very large, indeterminate numbers.  For instance,
 adding a constant to infinity gives another infinite result.
 
-Some operations are indeterminate and return \constant{NaN} or when the
+Some operations are indeterminate and return \constant{NaN}, or if the
 \exception{InvalidOperation} signal is trapped, raise an exception.  For
 example, \code{0/0} returns \constant{NaN} which means ``not a number''.  This
 variety of \constant{NaN} is quiet and, once created, will flow through other
@@ -909,11 +909,11 @@ result needs to interrupt a calculation for special handling.
 The signed zeros can result from calculations that underflow.
 They keep the sign that would have resulted if the calculation had
 been carried out to greater precision.  Since their magnitude is
-zero, the positive and negative zero are treated as equal and their
+zero, both positive and negative zeros are treated as equal and their
 sign is informational.
 
-In addition to the two signed zeros which are distinct, yet equal,
-there are various representations of zero with differing precisions,
+In addition to the two signed zeros which are distinct yet equal,
+there are various representations of zero with differing precisions
 yet equivalent in value.  This takes a bit of getting used to.  For
 an eye accustomed to normalized floating point representations, it
 is not immediately obvious that the following calculation returns