Merge pull request #18088 from Carreau/doc-wi

DOC: Update reference to verbatim in a few location.

7 files changed, 7 insertions, 7 deletions

diff --git a/numpy/polynomial/_polybase.py b/numpy/polynomial/_polybase.py
index 59c380f10..4462f9d6b 100644
--- a/numpy/polynomial/_polybase.py
+++ b/numpy/polynomial/_polybase.py
@@ -940,7 +940,7 @@ class ABCPolyBase(abc.ABC):
             also returned.
         w : array_like, shape (M,), optional
             Weights. If not None the contribution of each point
-            ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
+            ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the
             weights are chosen so that the errors of the products
             ``w[i]*y[i]`` all have the same variance.  The default value is
             None.
diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py
index 6745c9371..1149cdffa 100644
--- a/numpy/polynomial/chebyshev.py
+++ b/numpy/polynomial/chebyshev.py
@@ -1586,7 +1586,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None):
         information from the singular value decomposition is also returned.
     w : array_like, shape (`M`,), optional
         Weights. If not None, the contribution of each point
-        ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
+        ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the
         weights are chosen so that the errors of the products ``w[i]*y[i]``
         all have the same variance.  The default value is None.
 
diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py
index c679c5298..eef5c25b2 100644
--- a/numpy/polynomial/hermite.py
+++ b/numpy/polynomial/hermite.py
@@ -1311,7 +1311,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None):
         information from the singular value decomposition is also returned.
     w : array_like, shape (`M`,), optional
         Weights. If not None, the contribution of each point
-        ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
+        ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the
         weights are chosen so that the errors of the products ``w[i]*y[i]``
         all have the same variance.  The default value is None.
 
diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py
index 1ce8ebe04..05d1337b0 100644
--- a/numpy/polynomial/hermite_e.py
+++ b/numpy/polynomial/hermite_e.py
@@ -1302,7 +1302,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None):
         information from the singular value decomposition is also returned.
     w : array_like, shape (`M`,), optional
         Weights. If not None, the contribution of each point
-        ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
+        ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the
         weights are chosen so that the errors of the products ``w[i]*y[i]``
         all have the same variance.  The default value is None.
 
diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py
index 9cff0b71c..69d557510 100644
--- a/numpy/polynomial/laguerre.py
+++ b/numpy/polynomial/laguerre.py
@@ -1308,7 +1308,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None):
         information from the singular value decomposition is also returned.
     w : array_like, shape (`M`,), optional
         Weights. If not None, the contribution of each point
-        ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
+        ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the
         weights are chosen so that the errors of the products ``w[i]*y[i]``
         all have the same variance.  The default value is None.
 
diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py
index 427f9f82f..23ddd07ca 100644
--- a/numpy/polynomial/legendre.py
+++ b/numpy/polynomial/legendre.py
@@ -1328,7 +1328,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None):
         information from the singular value decomposition is also returned.
     w : array_like, shape (`M`,), optional
         Weights. If not None, the contribution of each point
-        ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
+        ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the
         weights are chosen so that the errors of the products ``w[i]*y[i]``
         all have the same variance.  The default value is None.
 
diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py
index 44784023b..940eed5e3 100644
--- a/numpy/polynomial/polynomial.py
+++ b/numpy/polynomial/polynomial.py
@@ -1253,7 +1253,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None):
         to solve the fit's matrix equation) is also returned.
     w : array_like, shape (`M`,), optional
         Weights. If not None, the contribution of each point
-        ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
+        ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the
         weights are chosen so that the errors of the products ``w[i]*y[i]``
         all have the same variance.  The default value is None.