Various corrections and enhancements to the documentation.

Change-Id: I03519bfccf4367b36d36006f1450d5fbcbbf8621
diff --git a/include/ceres/numeric_diff_cost_function.h b/include/ceres/numeric_diff_cost_function.h
index 6c44b58..a47a66d 100644
--- a/include/ceres/numeric_diff_cost_function.h
+++ b/include/ceres/numeric_diff_cost_function.h
@@ -82,14 +82,14 @@
 //
 //   CostFunction* cost_function
 //       = new NumericDiffCostFunction<MyScalarCostFunctor, CENTRAL, 1, 2, 2>(
-//           new MyScalarCostFunctor(1.0));                          ^  ^  ^
-//                                                               |   |  |  |
-//                                   Finite Differencing Scheme -+   |  |  |
-//                                   Dimension of residual ----------+  |  |
-//                                   Dimension of x --------------------+  |
-//                                   Dimension of y -----------------------+
+//           new MyScalarCostFunctor(1.0));                    ^     ^  ^  ^
+//                                                             |     |  |  |
+//                                 Finite Differencing Scheme -+     |  |  |
+//                                 Dimension of residual ------------+  |  |
+//                                 Dimension of x ----------------------+  |
+//                                 Dimension of y -------------------------+
 //
-// In this example, there is usually an instance for each measumerent of k.
+// In this example, there is usually an instance for each measurement of k.
 //
 // In the instantiation above, the template parameters following
 // "MyScalarCostFunctor", "1, 2, 2", describe the functor as computing
@@ -126,7 +126,7 @@
 // To get a numerically differentiated cost function, define a
 // subclass of CostFunction such that the Evaluate() function ignores
 // the jacobian parameter. The numeric differentiation wrapper will
-// fill in the jacobian parameter if nececssary by repeatedly calling
+// fill in the jacobian parameter if necessary by repeatedly calling
 // the Evaluate() function with small changes to the appropriate
 // parameters, and computing the slope. For performance, the numeric
 // differentiation wrapper class is templated on the concrete cost