diff --git a/c/mapping/mapper_stats2.c b/c/mapping/mapper_stats2.c
index 2eb482cf0..431b7934f 100644
--- a/c/mapping/mapper_stats2.c
+++ b/c/mapping/mapper_stats2.c
@@ -239,15 +239,15 @@ void stats2_corr_cov_get(void* pvstate, char* name1, char* name2, lrec_t* poutre
 			lrec_put(poutrec, key11, val11, LREC_FREE_ENTRY_KEY|LREC_FREE_ENTRY_VALUE);
 		}
 	} else if (pstate->do_which == DO_LINREG_PCA) {
-		char* keym   = mlr_paste_4_strings(name1, "_", name1, "_pca_m");
+		char* keym   = mlr_paste_4_strings(name1, "_", name2, "_pca_m");
 		char* keyb   = mlr_paste_4_strings(name1, "_", name2, "_pca_b");
-		char* keyq   = mlr_paste_4_strings(name2, "_", name1, "_pca_quality");
-		char* keyl1  = mlr_paste_4_strings(name2, "_", name1, "_pca_eival1");
-		char* keyl2  = mlr_paste_4_strings(name2, "_", name1, "_pca_eival2");
-		char* keyv11 = mlr_paste_4_strings(name2, "_", name1, "_pca_eivec11");
-		char* keyv12 = mlr_paste_4_strings(name2, "_", name1, "_pca_eivec12");
-		char* keyv21 = mlr_paste_4_strings(name2, "_", name1, "_pca_eivec21");
-		char* keyv22 = mlr_paste_4_strings(name2, "_", name1, "_pca_eivec22");
+		char* keyq   = mlr_paste_4_strings(name1, "_", name2, "_pca_quality");
+		char* keyl1  = mlr_paste_4_strings(name1, "_", name2, "_pca_eival1");
+		char* keyl2  = mlr_paste_4_strings(name1, "_", name2, "_pca_eival2");
+		char* keyv11 = mlr_paste_4_strings(name1, "_", name2, "_pca_eivec11");
+		char* keyv12 = mlr_paste_4_strings(name1, "_", name2, "_pca_eivec12");
+		char* keyv21 = mlr_paste_4_strings(name1, "_", name2, "_pca_eivec21");
+		char* keyv22 = mlr_paste_4_strings(name1, "_", name2, "_pca_eivec22");
 		if (pstate->count < 2LL) {
 			lrec_put(poutrec, keym,   "", LREC_FREE_ENTRY_KEY);
 			lrec_put(poutrec, keyb,   "", LREC_FREE_ENTRY_KEY);
diff --git a/c/test/expected/out b/c/test/expected/out
index 6f7e20c64..c9ae0039b 100644
--- a/c/test/expected/out
+++ b/c/test/expected/out
@@ -232,7 +232,7 @@ hat wye 9.000000  9.000000  1       9.000000  9.000000  0.031442 0.031442 1
 pan wye 10.000000 10.000000 1       10.000000 10.000000 0.502626 0.502626 1       0.502626 0.502626 0.952618 0.952618 1       0.952618 0.952618
 
 ./test/../mlr --opprint stats2 -a linreg-ols,linreg-pca,r2,corr,cov -f x,y,xy,y2,x2,x2 -g a,b ./test/input/abixy-wide
-a   b   x_y_ols_m x_y_ols_b x_x_pca_m x_y_pca_b y_x_pca_quality x_y_r2   x_y_corr  x_y_cov   xy_y2_ols_m xy_y2_ols_b xy_xy_pca_m xy_y2_pca_b y2_xy_pca_quality xy_y2_r2 xy_y2_corr xy_y2_cov x2_x2_ols_m x2_x2_ols_b x2_x2_pca_m x2_x2_pca_b x2_x2_pca_quality x2_x2_r2 x2_x2_corr x2_x2_cov
+a   b   x_y_ols_m x_y_ols_b x_y_pca_m x_y_pca_b x_y_pca_quality x_y_r2   x_y_corr  x_y_cov   xy_y2_ols_m xy_y2_ols_b xy_y2_pca_m xy_y2_pca_b xy_y2_pca_quality xy_y2_r2 xy_y2_corr xy_y2_cov x2_x2_ols_m x2_x2_ols_b x2_x2_pca_m x2_x2_pca_b x2_x2_pca_quality x2_x2_r2 x2_x2_corr x2_x2_cov
 cat pan 0.054420  0.481777  3.636062  -1.221602 0.177683        0.002504 0.050036  0.003777  0.950908    0.105754    1.715574    -0.081719   0.830612          0.435336 0.659800   0.041616  1.000000    0.000000    1.000000    0.000000    1.000000          1.000000 1.000000   0.066303
 pan wye -0.145486 0.584799  -1.340927 1.199920  0.254025        0.019479 -0.139568 -0.012683 0.908151    0.126628    1.595150    -0.045034   0.824114          0.438850 0.662457   0.046203  1.000000    0.000000    1.000000    0.000000    1.000000          1.000000 1.000000   0.093192
 wye cat 0.185913  0.377639  1.135325  -0.145894 0.309499        0.033002 0.181665  0.014494  0.969266    0.040602    1.406365    -0.081379   0.868480          0.561236 0.749157   0.052090  1.000000    0.000000    1.000000    0.000000    1.000000          1.000000 1.000000   0.086883
diff --git a/c/test/output/out b/c/test/output/out
index 6f7e20c64..c9ae0039b 100644
--- a/c/test/output/out
+++ b/c/test/output/out
@@ -232,7 +232,7 @@ hat wye 9.000000  9.000000  1       9.000000  9.000000  0.031442 0.031442 1
 pan wye 10.000000 10.000000 1       10.000000 10.000000 0.502626 0.502626 1       0.502626 0.502626 0.952618 0.952618 1       0.952618 0.952618
 
 ./test/../mlr --opprint stats2 -a linreg-ols,linreg-pca,r2,corr,cov -f x,y,xy,y2,x2,x2 -g a,b ./test/input/abixy-wide
-a   b   x_y_ols_m x_y_ols_b x_x_pca_m x_y_pca_b y_x_pca_quality x_y_r2   x_y_corr  x_y_cov   xy_y2_ols_m xy_y2_ols_b xy_xy_pca_m xy_y2_pca_b y2_xy_pca_quality xy_y2_r2 xy_y2_corr xy_y2_cov x2_x2_ols_m x2_x2_ols_b x2_x2_pca_m x2_x2_pca_b x2_x2_pca_quality x2_x2_r2 x2_x2_corr x2_x2_cov
+a   b   x_y_ols_m x_y_ols_b x_y_pca_m x_y_pca_b x_y_pca_quality x_y_r2   x_y_corr  x_y_cov   xy_y2_ols_m xy_y2_ols_b xy_y2_pca_m xy_y2_pca_b xy_y2_pca_quality xy_y2_r2 xy_y2_corr xy_y2_cov x2_x2_ols_m x2_x2_ols_b x2_x2_pca_m x2_x2_pca_b x2_x2_pca_quality x2_x2_r2 x2_x2_corr x2_x2_cov
 cat pan 0.054420  0.481777  3.636062  -1.221602 0.177683        0.002504 0.050036  0.003777  0.950908    0.105754    1.715574    -0.081719   0.830612          0.435336 0.659800   0.041616  1.000000    0.000000    1.000000    0.000000    1.000000          1.000000 1.000000   0.066303
 pan wye -0.145486 0.584799  -1.340927 1.199920  0.254025        0.019479 -0.139568 -0.012683 0.908151    0.126628    1.595150    -0.045034   0.824114          0.438850 0.662457   0.046203  1.000000    0.000000    1.000000    0.000000    1.000000          1.000000 1.000000   0.093192
 wye cat 0.185913  0.377639  1.135325  -0.145894 0.309499        0.033002 0.181665  0.014494  0.969266    0.040602    1.406365    -0.081379   0.868480          0.561236 0.749157   0.052090  1.000000    0.000000    1.000000    0.000000    1.000000          1.000000 1.000000   0.086883
diff --git a/doc/content-for-reference.html b/doc/content-for-reference.html
index f17cdbe12..262196d31 100644
--- a/doc/content-for-reference.html
+++ b/doc/content-for-reference.html
@@ -458,11 +458,7 @@ plotting; here&rsquo;s a screenshot.
 <img src="data/linreg-example.jpg"/>
 </center>
 
-<p/>
-(Perhaps surprisingly, the fit line doesn&rsquo;t have slope 1. This appears to
-be due to Miller&rsquo;s doing RMS minimization of vertical residuals; a PCA
-analysis gives slope 1 on the dominant eigenvector &mdash; thanks Drew Kunas for
-a good conversation!)
+<p/> (Thanks Drew Kunas for a good conversation about PCA!)
 
 <!-- ================================================================ -->
 <h2>step</h2>
diff --git a/doc/data/linreg-example.jpg b/doc/data/linreg-example.jpg
index 43eecce08..2579fbc28 100644
Binary files a/doc/data/linreg-example.jpg and b/doc/data/linreg-example.jpg differ
diff --git a/doc/data/linreg-example.txt b/doc/data/linreg-example.txt
index 80ca926f3..04ff92543 100644
--- a/doc/data/linreg-example.txt
+++ b/doc/data/linreg-example.txt
@@ -1,13 +1,14 @@
 
 mlr filter '($x<.5 && $y<.5) || ($x>.5 && $y>.5)' data/medium > data/medium-squares
 
-mlr --ofs newline stats2 -a linreg-ols -f x,y data/medium-squares
-x_y_ols_m=0.764675
-x_y_ols_b=0.124841
+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_ols_m and x_y_ols_b as shell variables
-eval $(mlr --ofs newline stats2 -a linreg-ols -f x,y data/medium-squares)
+# 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.
-mlr --onidx put '$yfit='$x_y_ols_m'*$x+'$x_y_ols_b then cut -x -f a,b,i data/medium-squares \
-  | pgr -p -title 'linreg-ols example'
+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
diff --git a/doc/reference.html b/doc/reference.html
index a4d09dae2..9165824f2 100644
--- a/doc/reference.html
+++ b/doc/reference.html
@@ -1177,16 +1177,17 @@ distributed on the unit interval. Here we remove half the data and fit a line to
 
 mlr filter '($x&lt;.5 &amp;&amp; $y&lt;.5) || ($x&gt;.5 &amp;&amp; $y&gt;.5)' data/medium &gt; data/medium-squares
 
-mlr --ofs newline stats2 -a linreg-ols -f x,y data/medium-squares
-x_y_ols_m=0.764675
-x_y_ols_b=0.124841
+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_ols_m and x_y_ols_b as shell variables
-eval $(mlr --ofs newline stats2 -a linreg-ols -f x,y data/medium-squares)
+# 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.
-mlr --onidx put '$yfit='$x_y_ols_m'*$x+'$x_y_ols_b then cut -x -f a,b,i data/medium-squares \
-  | pgr -p -title 'linreg-ols example'
+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
 </pre>
 </div>
 <p/>
@@ -1198,11 +1199,7 @@ plotting; here&rsquo;s a screenshot.
 <img src="data/linreg-example.jpg"/>
 </center>
 
-<p/>
-(Perhaps surprisingly, the fit line doesn&rsquo;t have slope 1. This appears to
-be due to Miller&rsquo;s doing RMS minimization of vertical residuals; a PCA
-analysis gives slope 1 on the dominant eigenvector &mdash; thanks Drew Kunas for
-a good conversation!)
+<p/> (Thanks Drew Kunas for a good conversation about PCA!)
 
 <!-- ================================================================ -->
 <h2>step</h2>  <a id="step"/>