X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;ds=inline;f=help%2Fhtml%2Fcalculations%2Fpca.html;h=66dbabd5705044b66a2a328b516e869cda69dfe2;hb=850c822ad4ec4cfbf59f65005c23fdc0b8a54512;hp=d109f0bdc76633ff2e4b000845a12df304707340;hpb=a8f483d04205bb8273ee311c12968b7e86d205fa;p=jalview.git
diff --git a/help/html/calculations/pca.html b/help/html/calculations/pca.html
index d109f0b..66dbabd 100755
--- a/help/html/calculations/pca.html
+++ b/help/html/calculations/pca.html
@@ -7,16 +7,18 @@
*
* Jalview is free software: you can redistribute it and/or
* modify it under the terms of the GNU General Public License
- * as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+ * as published by the Free Software Foundation, either version 3
+ * of the License, or (at your option) any later version.
*
* Jalview is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR
* PURPOSE. See the GNU General Public License for more details.
*
- * You should have received a copy of the GNU General Public License along with Jalview. If not, see .
+ * You should have received a copy of the GNU General Public License
+ * along with Jalview. If not, see .
* The Jalview Authors are detailed in the 'AUTHORS' file.
--->
+ -->
Principal Component Analysis
@@ -35,10 +37,10 @@ executing the calculation via a web service.
About PCA
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.