diff --git a/doc/data/linreg-example.txt b/doc/data/linreg-example.txt
index 04ff92543..b7ac713e3 100644
--- a/doc/data/linreg-example.txt
+++ b/doc/data/linreg-example.txt
@@ -1,14 +1,22 @@
 
+# Prepare input data:
 mlr filter '($x<.5 && $y<.5) || ($x>.5 && $y>.5)' data/medium > data/medium-squares
 
+# Do a linear regression and examine coefficients:
 mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares
 x_y_pca_m=1.014419
 x_y_pca_b=0.000308
 x_y_pca_quality=0.861354
 
-# Set x_y_pca_m and x_y_pca_b as shell variables
+# Option 1 to apply the regression coefficients and produce a linear fit:
+#   Set x_y_pca_m and x_y_pca_b as shell variables:
 eval $(mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares)
-
-# In addition to x and y, make a new yfit which is the line fit. Plot using your favorite tool.
+#   In addition to x and y, make a new yfit which is the line fit, then plot
+#   using your favorite tool:
 mlr --onidx put '$yfit='$x_y_pca_m'*$x+'$x_y_pca_b then cut -x -f a,b,i data/medium-squares \
   | pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
+
+# Option 2 to apply the regression coefficients and produce a linear fit: use --fit option
+mlr --onidx stats2 -a linreg-pca --fit -f x,y then cut -f a,b,i data/medium-squares \
+  | pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
+
diff --git a/doc/reference.html b/doc/reference.html
index 42643976a..8d53b8c03 100644
--- a/doc/reference.html
+++ b/doc/reference.html
@@ -2094,19 +2094,27 @@ distributed on the unit interval. Here we remove half the data and fit a line to
 <div class="pokipanel">
 <pre>
 
+# Prepare input data:
 mlr filter '($x&lt;.5 &amp;&amp; $y&lt;.5) || ($x&gt;.5 &amp;&amp; $y&gt;.5)' data/medium &gt; data/medium-squares
 
+# Do a linear regression and examine coefficients:
 mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares
 x_y_pca_m=1.014419
 x_y_pca_b=0.000308
 x_y_pca_quality=0.861354
 
-# Set x_y_pca_m and x_y_pca_b as shell variables
+# Option 1 to apply the regression coefficients and produce a linear fit:
+#   Set x_y_pca_m and x_y_pca_b as shell variables:
 eval $(mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares)
-
-# In addition to x and y, make a new yfit which is the line fit. Plot using your favorite tool.
+#   In addition to x and y, make a new yfit which is the line fit, then plot
+#   using your favorite tool:
 mlr --onidx put '$yfit='$x_y_pca_m'*$x+'$x_y_pca_b then cut -x -f a,b,i data/medium-squares \
   | pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
+
+# Option 2 to apply the regression coefficients and produce a linear fit: use --fit option
+mlr --onidx stats2 -a linreg-pca --fit -f x,y then cut -f a,b,i data/medium-squares \
+  | pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
+
 </pre>
 </div>
 <p/>