X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=help%2Fhtml%2Fcalculations%2Fpca.html;h=86097eae50d6c0586d2c146c1608bc8d9a1e0260;hb=141040cac1e8570634a25ad6a5550a56558041fb;hp=8d6f329041cae2459af19ae9f326780a00e022aa;hpb=e5cbd35e13f46ae1d59a08b0af1086677418141d;p=jalview.git

diff --git a/help/html/calculations/pca.html b/help/html/calculations/pca.html
index 8d6f329..86097ea 100755
--- a/help/html/calculations/pca.html
+++ b/help/html/calculations/pca.html
@@ -1,7 +1,7 @@
 <html>
 <!--
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.2)
- * Copyright (C) 2014 The Jalview Authors
+ * Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
+ * Copyright (C) $$Year-Rel$$ The Jalview Authors
  * 
  * This file is part of Jalview.
  * 
@@ -37,10 +37,10 @@ executing the calculation via a web service.</p>
 <p><strong>About PCA</strong></p>
 <p>Principal components analysis is a technique for examining the
 structure of complex data sets. The components are a set of dimensions
-formed from the measured values in the data set, and the principle
+formed from the measured values in the data set, and the principal
 component is the one with the greatest magnitude, or length. The sets of
 measurements that differ the most should lie at either end of this
-principle axis, and the other axes correspond to less extreme patterns
+principal axis, and the other axes correspond to less extreme patterns
 of variation in the data set.</p>
 
 	<p>
